transfer from old repo

1 PA Decline/00master.R

@@ -0,0 +1,252 @@
+## 
+## Master script
+## 
+## Based on the assignment work from the ISL-course
+## 
+## Generation 2 - 02.december.2022
+## Code preparation for analysis on Denmarks Statistics server with enriched data set.
+## 
+## Analysis plan:
+## Table 1
+## Figure 1: Sankey plot (drop & hop colored)
+## Table 2: Linear regression model of pase_6~.
+## Table 3: Elastic net prediction models of drop and hop. Performance measures referenced in text.
+## 
+## A Rmarkdown file could be created to write the initial report with main results.
+## This code is a bit of a mess, as it is the result of several iterations. It works however.
+## 
+
+## ====================================================================
+# Step 0: Primary outcome
+## ====================================================================
+
+# Script to run as hop and drop
+
+pout <- "drop" # Drop to first quartile
+
+# decl_rel
+# decl_abs
+# drop
+# hop
+
+## ====================================================================
+## Data
+## ====================================================================
+
+
+# setwd("/Users/au301842/PhysicalActivityandStrokeOutcome/1 PA Decline/")
+
+source(here::here("1 PA Decline/data_set.R"))
+# Loading data-set from USB, to not store on computer
+
+source(here::here("1 PA Decline/data_format.R"))
+
+## ====================================================================
+## 
+## Baseline - by PASE group
+## 
+## ====================================================================
+
+ts_q <- X_tbl |> 
+  select(vars) |> 
+  mutate(pase_0_cut = factor(quantile_cut(pase_0, groups = 4)[[1]],ordered = TRUE)) |> 
+  select(-pase_6,-pase_0) |> 
+  tbl_summary(missing = "no",
+                  by="pase_0_cut",
+                  value = list(where(is.factor) ~ "2"),
+                  type = list(mrs_0 ~ "categorical",
+                              all_continuous() ~ "continuous2"),
+                  statistic = list(all_continuous() ~ c("{N_nonmiss}",
+                                                        "{median} ({p25}, {p75})", 
+                                                        "{min}, {max}",
+                                                        "{mean} ({sd})"))
+) |> 
+  add_overall() |> 
+  add_n ()
+
+ts_q
+
+tbl_one_rtf <- file("table1.RTF", "w")
+writeLines(ts_q%>%as_gt()%>%as_rtf(), tbl_one_rtf)
+close(tbl_one_rtf)
+
+## ====================================================================
+# Drops and hops
+## ====================================================================
+
+# TRUEs are patients dropping
+table(X_tbl$pase_0_cut!="1"&X_tbl$pase_6_cut=="1")/nrow(X_tbl[X_tbl$pase_0_cut!="1",])
+
+# TRUEs are percentage of patients inactive before stroke being more active after
+table(X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1")/nrow(X_tbl[X_tbl$pase_0_cut=="1",])
+
+# TRUEs are percentage of patients being more active after that were inactive before stroke
+table(X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1")/nrow(X_tbl[X_tbl$pase_6_cut!="1",])
+
+# Difference between hop/no-hop
+t.test(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"])
+
+summary(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1","pase_0"])
+
+summary(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"])
+
+boxplot(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"])
+
+# Stationary low
+t.test(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_6"])
+
+boxplot(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_6"])
+
+## ====================================================================
+# Sankey plot
+## ====================================================================
+
+# source("sankey.R")
+# p_delta
+
+## ====================================================================
+# Six months PASE: Bivariate and multivariate analyses
+## ====================================================================
+
+dta_lmreg <- X_tbl |> 
+  select(vars) |> 
+  mutate(mrs_0=factor(ifelse(mrs_0==1,1,2))) 
+
+Hmisc::label(dta_lmreg$mrs_0) <- "Pre-stroke mRS >0"
+
+uv_reg <- tbl_uvregression(data=dta_lmreg, 
+                           method=lm, 
+                           y="pase_6",
+                           show_single_row = where(is.factor),
+                           estimate_fun = ~style_sigfig(.x,digits = 3),
+                           pvalue_fun = ~style_pvalue(.x, digits = 3)
+)
+
+mu_reg <- dta_lmreg |>
+  lm(formula=pase_6~.,data=_) |> 
+  tbl_regression(show_single_row = where(is.factor),
+                 estimate_fun = ~style_sigfig(.x,digits = 3),
+                 pvalue_fun = ~style_pvalue(.x, digits = 3)
+  )|> 
+  add_n()
+
+tbl_merge(list(uv_reg,mu_reg))
+
+
+## ====================================================================
+## 
+## Data variance
+## 
+## Illustrating principal components.
+## 
+## ====================================================================
+
+
+# source("PCA.R")
+# 
+# 
+# pca22
+# ggsave("pc_plot.png",width = 18, height = 12, dpi = 300, limitsize = TRUE, units = "cm")
+
+
+## ====================================================================
+## 
+## Models
+## 
+## ====================================================================
+
+
+# source("assign_full.R")
+
+ls <- list()
+for (i in c("drop","hop")){
+  pout <- i
+  source("data_format.R")
+  source("regularisation_steps.R")
+}
+
+# Loop to run regularised model on both drop and hop.
+# Saved in list for printing and exporting the plot.
+
+## ====================================================================
+# Step 1: data merge
+## ====================================================================
+tbl<-merge(ls$drop$RegularisedCoefs$'_data',ls$hop$RegularisedCoefs$'_data',by="name",all.x=T, sort=F)
+
+## ====================================================================
+# Step 2: table
+## ====================================================================
+com_coef_tbl<-tbl%>%
+  gt()%>%
+  fmt_number(
+    columns=colnames(tbl)[sapply(tbl,is.numeric)], ## Selecting all numeric
+    rows = everything(),
+    decimals = 3)%>%
+  tab_spanner(
+    label = "DROP",
+    columns = 2:5
+  )%>%
+  tab_spanner(
+    label = "HOP",
+    columns = 6:9
+  )%>%
+  tab_header(
+    title = "Model coefficients",
+    subtitle = "Combined table of both full and regularised model coefficients"
+  )
+
+
+# paste0("Regularised model, (a=",
+#        best_alph,
+#        ", l=",
+#        round(best_lamb,3),
+#        ")")
+
+com_coef_tbl
+
+## ====================================================================
+# Step 3: export
+## ====================================================================
+com_coef_rtf <- file("table2.RTF", "w")
+writeLines(com_coef_tbl%>%as_rtf(), com_coef_rtf)
+close(com_coef_rtf)
+
+
+
+## ====================================================================
+## 
+## Model performance
+## 
+## Table with performance meassures for the two different models.
+## 
+## ====================================================================
+
+## ====================================================================
+# Step 1: data set
+## ====================================================================
+
+tbl<-data.frame(Meassure=c(names(ls$drop$ConfusionMatrx$byClass),"Mean AUC"),
+                "Drop"=round(c(ls$drop$ConfusionMatrx$byClass,ls$drop$AUROC["Mean"]),3),
+                "Hop"=round(c(ls$hop$ConfusionMatrx$byClass,ls$hop$AUROC["Mean"]),3))
+
+## ====================================================================
+# Step 2: table
+## ====================================================================
+tbl_perf<-tbl%>%
+  gt()%>%
+  tab_header(
+    title = "Performance meassures",
+    subtitle = "Combined table of both drop and hop"
+  )
+
+tbl_perf
+
+## ====================================================================
+# Step 3: export
+## ====================================================================
+tbl_perf_rtf <- file("table3.RTF", "w")
+writeLines(tbl_perf%>%as_rtf(), tbl_perf_rtf)
+close(tbl_perf_rtf)
+
+
+

1 PA Decline/ESOC2023.qmd

@@ -0,0 +1,42 @@
+---
+title: "ESOC2023"
+format: html
+editor: visual
+---
+
+```{r}
+library(plotly)
+
+df
+
+fig <- plot_ly(
+    type = "sankey",
+    orientation = "h",
+
+    node = list(
+      label = c("A1", "A2", "B1", "B2", "C1", "C2"),
+      color = c("blue", "blue", "blue", "blue", "blue", "blue"),
+      pad = 15,
+      thickness = 20,
+      line = list(
+        color = "black",
+        width = 0.5
+      )
+    ),
+
+    link = list(
+      source = c(0,1,0,2,3,3),
+      target = c(2,3,3,4,4,5),
+      value =  c(8,4,2,8,4,2)
+    )
+  )
+fig <- fig %>% layout(
+    title = "Basic Sankey Diagram",
+    font = list(
+      size = 10
+    )
+)
+
+fig
+
+```

1 PA Decline/Fra DDV/241002/excluded.R

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+targets::tar_read(df_all_data_formatted) |>
+  get_vars(c("clin","lifestyle","ses", "assess.pred"))  |> 
+  dplyr::mutate(exclude=ifelse(is.na(pase_0)|is.na(pase_4),"Excluded","Included"))|> 
+  dplyr::select(-pase_0,-pase_4)  |>
+  dplyr::select(exclude,soc_status_nowork, fam_indk_hl, edu_level_hl)|> 
+  gtsummary::tbl_summary(by=exclude) |> 
+  gtsummary::add_p() |> 
+  fix_labels() |> 
+  mask_micro_summary(micro.n = 5)

1 PA Decline/Til DDV/00master.R

@@ -0,0 +1,251 @@
+## 
+## Master script
+## 
+## Based on the assignment work from the ISL-course
+## 
+## Generation 2 - 02.december.2022
+## Code preparation for analysis on Denmarks Statistics server with enriched data set.
+## 
+## Analysis plan:
+## Table 1
+## Figure 1: Sankey plot (drop & hop colored)
+## Table 2: Linear regression model of pase_6~.
+## Table 3: Elastic net prediction models of drop and hop. Performance measures referenced in text.
+## 
+## A Rmarkdown file could be created to write the initial report with main results.
+## This code is a bit of a mess, as it is the result of several iterations. It works however.
+## 
+
+## ====================================================================
+# Step 0: Primary outcome
+## ====================================================================
+
+# Script to run as hop and drop
+
+pout <- "drop" # Drop to first quartile
+
+# decl_rel
+# decl_abs
+# drop
+# hop
+
+## ====================================================================
+## Data
+## ====================================================================
+
+
+setwd("/Users/au301842/PhysicalActivityandStrokeOutcome/1 PA Decline/")
+
+source("data_set.R")
+
+source("data_format.R")
+
+## ====================================================================
+## 
+## Baseline - by PASE group
+## 
+## ====================================================================
+
+ts_q <- X_tbl |> 
+  select(vars) |> 
+  mutate(pase_0_cut = factor(quantile_cut(pase_0, groups = 4)[[1]],ordered = TRUE)) |> 
+  select(-pase_6,-pase_0) |> 
+  tbl_summary(missing = "no",
+                  by="pase_0_cut",
+                  value = list(where(is.factor) ~ "2"),
+                  type = list(mrs_0 ~ "categorical",
+                              all_continuous() ~ "continuous2"),
+                  statistic = list(all_continuous() ~ c("{N_nonmiss}",
+                                                        "{median} ({p25}, {p75})", 
+                                                        "{min}, {max}",
+                                                        "{mean} ({sd})"))
+) |> 
+  add_overall() |> 
+  add_n ()
+
+ts_q
+
+tbl_one_rtf <- file("table1.RTF", "w")
+writeLines(ts_q%>%as_gt()%>%as_rtf(), tbl_one_rtf)
+close(tbl_one_rtf)
+
+## ====================================================================
+# Drops and hops
+## ====================================================================
+
+# TRUEs are patients dropping
+table(X_tbl$pase_0_cut!="1"&X_tbl$pase_6_cut=="1")/nrow(X_tbl[X_tbl$pase_0_cut!="1",])
+
+# TRUEs are percentage of patients inactive before stroke being more active after
+table(X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1")/nrow(X_tbl[X_tbl$pase_0_cut=="1",])
+
+# TRUEs are percentage of patients being more active after that were inactive before stroke
+table(X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1")/nrow(X_tbl[X_tbl$pase_6_cut!="1",])
+
+# Difference between hop/no-hop
+t.test(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"])
+
+summary(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1","pase_0"])
+
+summary(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"])
+
+boxplot(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"])
+
+# Stationary low
+t.test(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_6"])
+
+boxplot(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_6"])
+
+## ====================================================================
+# Sankey plot
+## ====================================================================
+
+# source("sankey.R")
+# p_delta
+
+## ====================================================================
+# Six months PASE: Bivariate and multivariate analyses
+## ====================================================================
+
+dta_lmreg <- X_tbl |> 
+  select(vars) |> 
+  mutate(mrs_0=factor(ifelse(mrs_0==1,1,2))) 
+
+Hmisc::label(dta_lmreg$mrs_0) <- "Pre-stroke mRS >0"
+
+uv_reg <- tbl_uvregression(data=dta_lmreg, 
+                           method=lm, 
+                           y="pase_6",
+                           show_single_row = where(is.factor),
+                           estimate_fun = ~style_sigfig(.x,digits = 3),
+                           pvalue_fun = ~style_pvalue(.x, digits = 3)
+)
+
+mu_reg <- dta_lmreg |>
+  lm(formula=pase_6~.,data=_) |> 
+  tbl_regression(show_single_row = where(is.factor),
+                 estimate_fun = ~style_sigfig(.x,digits = 3),
+                 pvalue_fun = ~style_pvalue(.x, digits = 3)
+  )|> 
+  add_n()
+
+tbl_merge(list(uv_reg,mu_reg))
+
+
+## ====================================================================
+## 
+## Data variance
+## 
+## Illustrating principal components.
+## 
+## ====================================================================
+
+
+# source("PCA.R")
+# 
+# 
+# pca22
+# ggsave("pc_plot.png",width = 18, height = 12, dpi = 300, limitsize = TRUE, units = "cm")
+
+
+## ====================================================================
+## 
+## Models
+## 
+## ====================================================================
+
+
+# source("assign_full.R")
+
+ls <- list()
+for (i in c("drop","hop")){
+  pout <- i
+  source("data_format.R")
+  source("regularisation_steps.R")
+}
+
+# Loop to run regularised model on both drop and hop.
+# Saved in list for printing and exporting the plot.
+
+## ====================================================================
+# Step 1: data merge
+## ====================================================================
+tbl<-merge(ls$drop$RegularisedCoefs$'_data',ls$hop$RegularisedCoefs$'_data',by="name",all.x=T, sort=F)
+
+## ====================================================================
+# Step 2: table
+## ====================================================================
+com_coef_tbl<-tbl%>%
+  gt()%>%
+  fmt_number(
+    columns=colnames(tbl)[sapply(tbl,is.numeric)], ## Selecting all numeric
+    rows = everything(),
+    decimals = 3)%>%
+  tab_spanner(
+    label = "DROP",
+    columns = 2:5
+  )%>%
+  tab_spanner(
+    label = "HOP",
+    columns = 6:9
+  )%>%
+  tab_header(
+    title = "Model coefficients",
+    subtitle = "Combined table of both full and regularised model coefficients"
+  )
+
+
+# paste0("Regularised model, (a=",
+#        best_alph,
+#        ", l=",
+#        round(best_lamb,3),
+#        ")")
+
+com_coef_tbl
+
+## ====================================================================
+# Step 3: export
+## ====================================================================
+com_coef_rtf <- file("table2.RTF", "w")
+writeLines(com_coef_tbl%>%as_rtf(), com_coef_rtf)
+close(com_coef_rtf)
+
+
+
+## ====================================================================
+## 
+## Model performance
+## 
+## Table with performance meassures for the two different models.
+## 
+## ====================================================================
+
+## ====================================================================
+# Step 1: data set
+## ====================================================================
+
+tbl<-data.frame(Meassure=c(names(ls$drop$ConfusionMatrx$byClass),"Mean AUC"),
+                "Drop"=round(c(ls$drop$ConfusionMatrx$byClass,ls$drop$AUROC["Mean"]),3),
+                "Hop"=round(c(ls$hop$ConfusionMatrx$byClass,ls$hop$AUROC["Mean"]),3))
+
+## ====================================================================
+# Step 2: table
+## ====================================================================
+tbl_perf<-tbl%>%
+  gt()%>%
+  tab_header(
+    title = "Performance meassures",
+    subtitle = "Combined table of both drop and hop"
+  )
+
+tbl_perf
+
+## ====================================================================
+# Step 3: export
+## ====================================================================
+tbl_perf_rtf <- file("table3.RTF", "w")
+writeLines(tbl_perf%>%as_rtf(), tbl_perf_rtf)
+close(tbl_perf_rtf)
+
+
+

1 PA Decline/Til DDV/data_format.R

@@ -0,0 +1,50 @@
+## Article 1 outcome group definition script
+## To be enriched from Statistics Denmark
+## 
+## Based on the ItMLiHSmar2022 course
+
+library(Hmisc)
+library(dplyr)
+library(daDoctoR)
+library(tidyselect)
+
+# Setting final primary output from "pout"
+if (pout=="drop"){
+  X_tbl <- X_tbl|>
+    mutate(group=pase_drop_fac)
+  
+  # print(quantile(as.numeric(X_tbl$pase_0)))
+  # print(quantile(as.numeric(X_tbl$pase_6)))
+  # print(summary(X_tbl$pase_0_cut))
+  
+  X_tbl_f <- X_tbl|>
+    filter(pase_0_cut!=1)|>
+    select(-starts_with("pase_"))
+}
+
+if (pout=="hop"){
+  X_tbl <- X_tbl|>
+    mutate(group=pase_hop_fac)
+  
+  # print(quantile(as.numeric(X_tbl$pase_0)))
+  # print(quantile(as.numeric(X_tbl$pase_6)))
+  # print(summary(X_tbl$pase_0_cut))
+  
+  X_tbl_f <- X_tbl|>
+    filter(pase_6_cut!=1)|>
+    select(-starts_with("pase_"))
+}
+
+# Dropping non-complete for analysis
+Xy <- X_tbl_f|> 
+  na.omit()|> # Keeping only complete observations
+  select(-c(tci) # Left out of model as no present in drop-group
+  )|>
+  mutate(mrs_0=factor(ifelse(mrs_0==1,1,2))) # Sets binary mRS 0 to include in glmnet, 0 or above
+
+label(Xy) = as.list(var.labels[match(names(Xy), names(var.labels))])
+
+X<-dplyr::select(Xy,-c(group, -starts_with("pase_")) # Exclude primary outcome
+                 )
+y<-Xy$group
+

1 PA Decline/Til DDV/data_set.R

@@ -0,0 +1,193 @@
+## Article 1 data set definition
+## To be enriched from Statistics Denmark
+## 
+## Based on the ItMLiHSmar2022 course
+
+library(Hmisc)
+library(dplyr)
+library(daDoctoR)
+library(tidyverse)
+library(patchwork)
+library(caret)
+library(glmnet)
+library(leaps)
+library(pROC)
+library(gt)
+library(gtsummary)
+library(glue)
+# library(ggdendro)
+library(corrplot)
+
+## ====================================================================
+# Step 2: Selection
+## ====================================================================
+
+
+export<-export[,c("pase_0",
+               "age",
+               "sex",
+               "civil",
+               "smoke_ever", 
+               "smoker",
+               "rtreat",
+               "alc",
+               "afli",
+               "hypertension",
+               "diabetes",
+               "mrs_0",
+               "nihss_c",
+               "thrombolysis",
+               "pad", 
+               "thrombechtomy", 
+               "ami", 
+               "tci", 
+               "pase_6")]
+
+## ====================================================================
+# Step 3: Formatting variables
+## ====================================================================
+
+export$diabetes[is.na(export$diabetes)]<-"no"
+export$diabetes[is.na(export$hypertension)]<-"no"
+export$thrombolysis[is.na(export$thrombolysis)]<-"no"
+export$thrombechtomy[is.na(export$thrombechtomy)]<-"no"
+export$pad[is.na(export$pad)]<-"no"
+export$ami[is.na(export$ami)]<-"no"
+# export$smoker_prev <- ifelse(export$smoker=="3","yes","no")
+export$smoker <- ifelse(export$smoker=="1","yes","no")
+export$smoker[is.na(export$smoker)] <- "no"
+# export$mrs_0[export$mrs_0==3]<-NA
+
+dta <- export %>% 
+  # as_tibble()%>%
+  mutate(any_rep=factor(ifelse(thrombolysis=="yes"|thrombechtomy=="yes","yes","no")), # If not noted, no therapy was received
+         male_sex= factor(ifelse(sex=="female","no","yes")),
+         # smoke_ever=factor(ifelse(smoke_ever=="never","no","yes")),
+         civil=factor(ifelse(civil=="partner","no","yes")), # Sets "yes" for not-cohabiting
+         rtreat=factor(ifelse(rtreat=="Placebo","no","yes")), # "Yes" receives active treatment
+         alc=factor(ifelse(alc=="more","yes","no")), # Yes for more than guideline
+         pase_0=as.numeric(pase_0),
+         pase_6=as.numeric(pase_6),
+         across(c("diabetes",
+                  "hypertension",
+                  "smoker",
+                  "afli",
+                  "pad",
+                  "ami",
+                  "tci",
+                  "mrs_0"),as.factor),
+         across(c("nihss_c",
+                  "age"),as.numeric )
+  )%>% 
+  select(-c(sex))
+
+
+## ====================================================================
+# Step 4: Defining outcome
+## ====================================================================
+
+## Changed to step 7
+## This is to perform proper quantile split based on actually included.
+
+## ====================================================================
+# Step 5: Ordering variables
+## ====================================================================
+
+vars <- c("age",
+          "male_sex",
+          "civil",
+          "pase_0",
+          "smoker",
+          "alc",
+          "afli",
+          "hypertension",
+          "diabetes",
+          "pad",
+          "ami",
+          "tci",
+          "mrs_0",
+          "nihss_c",
+          "any_rep",
+          "rtreat",
+          "pase_6")
+
+dta<-dta[vars]
+
+## ====================================================================
+# Step 6: Labeling
+## ====================================================================
+
+var.labels = c(age="Age",
+               male_sex="Male",
+               civil="Living alone",
+               pase_0="Pre-stroke PASE score",
+               pase_6="Six month PASE score",
+               smoker="Daily or occasinally smoking",
+               alc="More alcohol than recommendation",
+               afli="AFIB",
+               hypertension="Hypertension",
+               diabetes="Diabetes",
+               pad="PAD",
+               ami="Previous MI",
+               tci="Previous TIA",
+               mrs_0="Pre-stroke mRS  [-1]",
+               nihss_c="Acute NIHSS score",
+               thrombolysis="Acute thrombolysis",
+               thrombechtomy="Acute thrombechtomy",
+               any_rep="Any reperfusion therapy",
+               rtreat="Active trial treatment",
+               pase_drop_fac="PASE first quartile drop F",
+               pase_hop_fac="PASE first quartile hop F",
+               pase_0_cut="PASE 0 quartiles",
+               pase_6_cut="PASE 6 quartiles")
+
+
+
+## ====================================================================
+# Step 7: final data export
+## ====================================================================
+
+data_summary<-summary(dta)
+
+# Saving "old" factorised variables
+sel<-sapply(dta,is.factor)
+# Reformatting factors as 1/2 for analysis
+dta<-dta |> 
+  mutate(across(where(is.factor), as.numeric))|> # Turning factors into 1(no) or 2(yes) for model. Numbered alphabetically.
+  mutate(across(matches(colnames(dta)[sel]), as.factor),
+         across(starts_with("pase_"), as.numeric))
+
+# Filtering out non-PASE
+X_tbl<-dta |>
+  filter(!is.na(pase_0),!is.na(pase_6))
+
+nrow(X_tbl)
+
+# Defining possible outcome meassures. Keeping in df for characterisation
+X_tbl <- X_tbl|>
+  mutate(## Relative decline
+    pase_diff=(pase_0-pase_6),
+    pase_decl_rel = pase_diff/pase_0*100,
+    # pase_decl_rel_fac=factor(ifelse(pase_decl_rel>=rel_dif,"yes","no")),
+    ## Absolute decline
+    # pase_decl_abs_fac=factor(ifelse(pase_diff>=abs_dif,"yes","no")),
+    ## Drop
+    pase_0_cut=quantile_cut(as.numeric(pase_0),
+                            groups=4,
+                            group.names = c(as.character(1:4)),
+                            y=as.numeric(pase_0),
+                            ordered.f = TRUE,
+                            inc.outs = TRUE, 
+                            detail.lst=FALSE),
+    pase_6_cut=quantile_cut(as.numeric(pase_6),
+                            groups=4,
+                            group.names = c(as.character(1:4)),
+                            y=as.numeric(pase_0),
+                            ordered.f = TRUE,
+                            inc.outs = TRUE, 
+                            detail.lst=FALSE),
+    pase_drop_fac=factor(ifelse(pase_6_cut==1&pase_0_cut!=1,"yes","no")),
+    pase_hop_fac=factor(ifelse(pase_6_cut!=1&pase_0_cut==1,"yes","no")))
+
+Hmisc::label(X_tbl) = as.list(var.labels[match(names(X_tbl), names(var.labels))])
+

1 PA Decline/Til DDV/regular_fun.R

@@ -0,0 +1,117 @@
+## ItMLiHSmar2022
+## regular_fun.R, child script
+## Regularisation model building function
+## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk
+## 
+## Now modified to use in publication
+## 
+
+regular_fun<-function(X,y,K,lambdas,alpha){
+n<-nrow(X)
+set.seed(321)
+
+# Using caret function to ensure both levels represented in all folds
+c<-createFolds(y=y, k = K, list = FALSE, returnTrain = TRUE) 
+
+B<-yhatTestProbKeep<-list()
+accTrain<-accTest<-err_train<-err_test<-auc_train<-auc_test<-matrix(nrow = K,ncol = length(lambdas))
+
+catinfo<-levels(y)
+
+cMatTrain<-cMatTest<-table(true=factor(c(0,0),levels=catinfo),pred=factor(c(0,0),levels=catinfo))
+
+
+## Iterate over partitions
+for (idx1 in 1:K){
+  
+  # Status
+  cat('Processing fold', idx1, 'of', K,'\n')
+  
+  # idx1=1
+  # Get training- and test sets
+  I_train = c!=idx1 ## Creating selection vector of TRUE/FALSE
+  I_test = !I_train
+  
+  Xtrain = X[I_train,]
+  ytrain = y[I_train]
+  Xtest = X[I_test,]
+  ytest = y[I_test]
+  
+  
+  ## Model matrices for glmnet
+  ## Using the complicated approach not to include first level.
+  # Xmat.train<-model.matrix(~ .-1, data=Xtrain, 
+  #              contrasts.arg = lapply(Xtrain[,sapply(Xtrain, is.factor)], 
+  #                                     contrasts, contrasts=T))
+  # Xmat.test<-model.matrix(~ .-1, data=Xtest, 
+  #                contrasts.arg = lapply(Xtest[,sapply(Xtest, is.factor)], 
+  #                                       contrasts, contrasts=T))
+  
+  # Xmat.train<-model.matrix(~.-1,Xtrain)
+  # Xmat.test<-model.matrix(~.-1,Xtest)
+  
+  # Weights
+  ytrain_weight<-as.vector(1 - (table(ytrain)[ytrain] / length(ytrain)))
+  # ytest_weight<-as.vector(1 / (table(ytest)[ytest] / length(ytest)))
+  
+  # Fit regularized linear regression model
+  mod<-glmnet(Xtrain, ytrain, 
+              alpha = alpha,        ## Alpha = 1 for lasso
+              lambda = lambdas, ## Setting lambdas
+              standardize = TRUE, ## Scales and centers
+              weights = ytrain_weight,
+              family = "binomial"
+              )
+  
+  # Keep coefficients for plot
+  B[[idx1]] <- as.matrix(coef(mod))
+  
+  # Iterate over regularization strengths to compute training- and test
+  # errors for individual regularization strengths.
+  for (idx2 in 1:length(lambdas)){
+    # idx2=1
+    
+    # Predict
+    yhatTrainProb<-predict(mod, 
+                           s = lambdas[idx2], 
+                           newx = data.matrix(Xtrain),
+                           type = "response"
+    )
+    
+    yhatTestProb<-predict(mod, 
+                          s = lambdas[idx2], 
+                          newx = data.matrix(Xtest),
+                          type = "response"
+    )
+    
+    # Compute training and test error
+    yhatTrain = round(yhatTrainProb)
+    yhatTest = round(yhatTestProb)
+    
+    # Make predictions categorical again (instead of 0/1 coding)
+    yhatTrainCat = factor(round(yhatTrainProb),levels=c("0","1"),labels=catinfo,ordered = TRUE)
+    yhatTestCat = factor(round(yhatTestProb),levels=c("0","1"),labels=catinfo,ordered = TRUE)
+    
+    # Evaluate classifier performance
+    # Accuracy
+    # accTrain[idx1,idx2] <- sum(yhatTrainCat==ytrain)/length(ytrain)
+    # accTest [idx1,idx2] <- sum(yhatTestCat==ytest)/length(ytest)
+    # #
+    # # Error rate
+    # err_train[idx1,idx2] =  1 - accTrain[idx1,idx2]
+    # err_test [idx1,idx2] =   1 - accTest[idx1,idx2]
+    
+    # AUROC
+    suppressMessages(
+      auc_train[idx1,idx2]<-auc(ytrain, yhatTrainCat))
+    suppressMessages(
+      auc_test [idx1,idx2]<-auc(ytest, yhatTestCat))
+    
+    # Compute confusion matrices
+    cMatTrain = cMatTrain + table(true=ytrain,pred=yhatTrainCat)
+    cMatTest = cMatTest + table(true=ytest,pred=yhatTestCat)
+  }
+}
+ls<-list(mod=mod,B=B,auc_train=auc_train,auc_test=auc_test,cMatTrain=cMatTrain,cMatTest=cMatTest)
+return(ls)
+}

1 PA Decline/Til DDV/regularisation_steps.R

@@ -0,0 +1,150 @@
+## ItMLiHSmar2022
+## regularisation_steps.R, child script
+## Regularised model building and analysation for assignment
+## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk
+## 
+## Now modified to use in publication
+## 
+
+## ====================================================================
+## Step 0: data import and wrangling
+## ====================================================================
+
+setwd("/Users/au301842/PhysicalActivityandStrokeOutcome/1 PA Decline/")
+
+# source("data_format.R")
+y1<-factor(as.integer(y)-1) ## Outcome is required to be factor of 0 or 1.
+
+
+## ====================================================================
+## Step 1: settings
+## ====================================================================
+
+## Folds
+K=10
+set.seed(3)
+c<-caret::createFolds(y=y, 
+                      k = K, 
+                      list = FALSE, 
+                      returnTrain = TRUE) # Foldids for alpha tuning
+
+## Defining tuning parameters
+lambdas=2^seq(-10, 5, 1)
+alphas<-seq(0,1,.1)
+
+## Weights for models
+weighted=TRUE
+if (weighted == TRUE) {
+  wght<-as.vector(1 - (table(y)[y] / length(y)))
+} else {
+  wght <- rep(1, nrow(y))
+}
+
+
+## Standardise numeric
+## Centered and 
+
+
+
+## ====================================================================
+## Step 2: all cross validations for each alpha
+## ====================================================================
+
+library(furrr)
+library(purrr)
+library(doMC)
+registerDoMC(cores=6)
+
+# Nested CVs with analysis for all lambdas for each alpha
+# 
+set.seed(3)
+cvs <- future_map(alphas, function(a){
+  cv.glmnet(model.matrix(~.-1,X),
+            y1,
+            weights = wght,
+            lambda=lambdas, 
+            type.measure = "deviance", # This is standard measure and recommended for tuning
+            foldid = c, # Per recommendation the folds are kept for alpha optimisation
+            alpha=a,
+            standardize=TRUE,
+            family=quasibinomial,
+            keep=TRUE) # Same as binomial, but not as picky
+})
+
+## ====================================================================
+# Step 3: optimum lambda for each alpha
+## ====================================================================
+
+
+# For each alpha, lambda is chosen for the lowest meassure (deviance)
+each_alpha <- sapply(seq_along(alphas), function(id) {
+  each_cv <- cvs[[id]]
+  alpha_val <- alphas[id]
+  index_lmin <- match(each_cv$lambda.min, 
+                      each_cv$lambda)
+  c(lamb = each_cv$lambda.min, 
+    alph = alpha_val,
+    cvm = each_cv$cvm[index_lmin])
+})
+
+# Best lambda
+best_lamb <- min(each_alpha["lamb", ])
+
+# Alpha is chosen for best lambda with lowest model deviance, each_alpha["cvm",]
+best_alph <- each_alpha["alph",][each_alpha["cvm",]==min(each_alpha["cvm",]
+                                                         [each_alpha["lamb",] %in% best_lamb])]
+
+## https://stackoverflow.com/questions/42007313/plot-an-roc-curve-in-r-with-ggplot2
+p_roc<-roc.glmnet(cvs[[1]]$fit.preval, newy = y)[[match(best_alph,alphas)]]|> # Plots performance from model with best alpha
+  ggplot(aes(FPR,TPR)) + 
+  geom_step() +
+  coord_cartesian(xlim=c(0,1), ylim=c(0,1)) +
+  geom_abline()+
+  theme_bw()
+
+## ====================================================================
+# Step 4: Creating the final model
+## ====================================================================
+
+source("regular_fun.R") # Custom function
+optimised_model<-regular_fun(X,y1,K,lambdas=best_lamb,alpha=best_alph) 
+# With lambda and alpha specified, the function is just a k-fold cross-validation wrapper, 
+# but keeps model performance figures from each fold.
+
+list2env(optimised_model,.GlobalEnv)
+# Function outputs a list, which is unwrapped to Env.
+# See source script for reference.
+
+## ====================================================================
+# Step 5: creating table of coefficients for inference
+## ====================================================================
+
+Bmatrix<-matrix(unlist(B),ncol=10)
+Bmedian<-apply(Bmatrix,1,median)
+Bmean<-apply(Bmatrix,1,mean)
+
+reg_coef_tbl<-tibble(
+  name = c("Intercept",Hmisc::label(X)),
+  medianX = round(Bmedian,5),
+  ORmed = round(exp(Bmedian),5),
+  meanX = round(Bmean,5),
+  ORmea = round(exp(Bmean),5))%>%
+  # arrange(desc(abs(medianX)))%>%
+  gt()
+
+## ====================================================================
+# Step 6: plotting predictive performance
+## ====================================================================
+
+reg_cfm<-confusionMatrix(cMatTest)
+reg_auc_sum<-summary(auc_test[,1])
+
+## ====================================================================
+# Step 7: Packing list to save in loop
+## ====================================================================
+
+ls[[i]] <- list("RegularisedCoefs"=reg_coef_tbl,
+                "bestA"=best_alph,
+                "bestL"=best_lamb,
+                "ConfusionMatrx"=reg_cfm,
+                "AUROC"=reg_auc_sum)

1 PA Decline/Til DDV/standardise.R

@@ -0,0 +1,41 @@
+## ItMLiHSmar2022
+## standardise.R, child script
+## Data standardisation, returns list
+## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk
+
+standardise<-function(train,test,type){
+  # From:
+  # https://datascience.stackexchange.com/questions/13971/standardization-normalization-test-data-in-r
+  
+  sel<-sapply(Xtrain,is.numeric) # Deciding which to stadardise (only numeric)
+  cnm<-colnames(Xtrain) # Saving column names for ordering
+  
+  # Subsetting
+  
+  ## Data to treat
+  train.tr<-train[,sel]
+  test.tr<-test[,sel]
+  
+  ## Data to save
+  train.sv<-train[,!sel]
+  test.sv<-test[,!sel]
+  
+  # Calculate mean and SD of train data
+  trainMean <- sapply(train.tr,mean)
+  trainSd <- sapply(train.tr,sd)
+  
+  if (type=="c"){
+    ## centered
+    norm.trainData<-sweep(train.tr, 2L, trainMean) # using the default "-" to subtract mean column-wise   
+    norm.testData<-sweep(test.tr, 2L, trainMean) # using the default "-" to subtract mean column-wise   
+  }
+ 
+  if (type=="cs"){
+    ## centered AND scaled (Z-score standardisation)
+    norm.trainData<-sweep(sweep(train.tr, 2L, trainMean), 2, trainSd, "/")
+    norm.testData<-sweep(sweep(test.tr, 2L, trainMean), 2, trainSd, "/")
+  }
+  return(list(XtrainSt=cbind(norm.trainData,train.sv)[,cnm], # Reordering columns to original
+              XtestSt=cbind(norm.testData,test.sv)[,cnm]))
+}
+

1 PA Decline/archive/dataset.R

@@ -0,0 +1,98 @@
+# Data
+## Import from previous work
+dta<-read.csv("/Volumes/Data/exercise/source/background.csv",na.strings = c("NA","","unknown"),colClasses = "character")
+
+## Cleaning and enhancing
+dta$pase_drop<-factor(ifelse((dta$pase_0_q=="q_2"|dta$pase_0_q=="q_3"|dta$pase_0_q=="q_4")&dta$pase_06_q=="q_1","yes","no"),levels = c("no","yes"))
+dta$pase_drop[is.na(dta$pase_6)]<-NA
+dta$pase_drop[is.na(dta$pase_0)]<-NA
+
+## Selection of data set and formatting
+library(dplyr)
+dta_f<-dta %>% filter(pase_0_q != "q_1" & !is.na(pase_drop))
+
+
+variable_names<-c("age","sex","weight","height",
+                     "bmi",
+                     "smoke_ever",
+                     "civil",
+                     "diabetes", 
+                     "hypertension",
+                     "pad", 
+                     "afli",  
+                     "ami", 
+                     "tci",
+                     "nihss_0",
+                     "thrombolysis", 
+                     "thrombechtomy",
+                     "rep_any","pase_0_q","pase_drop")
+
+
+library(daDoctoR)
+dta2<-dta_f[,variable_names]
+
+dta2<-col_num(c("age","weight","height","bmi","nihss_0"),dta2)
+dta2<-col_fact(c("sex","smoke_ever","civil","diabetes", "hypertension","pad", "afli",  "ami", "tci","thrombolysis", "thrombechtomy","rep_any","pase_0_q","pase_drop"),dta2)
+
+## Partitioning
+library(caret)
+set.seed(100)
+
+## Step 1: Get row numbers for the training data
+trainRowNumbers <- createDataPartition(dta2$pase_drop, p=0.8, list=FALSE)
+
+## Step 2: Create the training  dataset
+trainData <- dta2[trainRowNumbers,]
+
+## Step 3: Create the test dataset
+testData <- dta2[-trainRowNumbers,]
+y_test = testData[,"pase_drop"]
+
+# Store X and Y for later use.
+x = trainData %>% select(!matches("pase_drop"))
+y = trainData[,"pase_drop"]
+
+# Normalization and dummy binaries
+
+# One-Hot Encoding
+# Creating dummy variables is converting a categorical variable to as many binary variables as here are categories.
+dummies_model <- dummyVars(pase_drop ~ ., data=trainData)
+
+# Create the dummy variables using predict. The Y variable (Purchase) will not be present in trainData_mat.
+trainData_mat <- predict(dummies_model, newdata = trainData)
+
+# # Convert to dataframe
+trainData <- data.frame(trainData_mat)
+
+# # See the structure of the new dataset
+str(trainData)
+
+dummies_model <- dummyVars(pase_drop ~ ., data=testData)
+testData_mat <- predict(dummies_model, newdata = testData)
+testData <- data.frame(testData_mat)
+preProcess_range_model <- preProcess(testData, method='range')
+testData <- predict(preProcess_range_model, newdata = testData)
+testData$pase_drop<-y_test
+
+# Imputation
+
+library(RANN)  # required for knnInpute
+preProcess_missingdata_model <- preProcess(trainData, method='knnImpute')
+# preProcess_missingdata_model
+
+trainData <- predict(preProcess_missingdata_model, newdata = trainData)  # Giver fejl??
+anyNA(trainData)
+
+# skimr::skim(trainData)
+# skimr::skim(x)
+
+preProcess_range_model <- preProcess(trainData, method='range')
+trainData <- predict(preProcess_range_model, newdata = trainData)
+
+# Append the Y variable
+trainData$pase_drop <- y
+
+
+# Export
+write.csv(trainData,"/Users/au301842/PhysicalActivityandStrokeOutcome/data/trainData.csv",row.names = FALSE)
+write.csv(testData,"/Users/au301842/PhysicalActivityandStrokeOutcome/data/testData.csv",row.names = FALSE)

1 PA Decline/archive/generation_1/00master.R

@@ -0,0 +1,409 @@
+## 
+## Master script
+## 
+## Based on the assignment work from the ISL-course
+## 
+## 
+## 
+
+## ====================================================================
+# Step 0: Primary outcome
+## ====================================================================
+
+# Difs
+rel_dif <- 20 # 20 % difference
+abs_dif <- 20 # 20 point diff
+
+# pout <- "diff"
+# 
+# Note:: By increasing the relative decline, the sensitivity increases and specificity declines.
+# This fact is an argument against over fitting. The reason being the nature of the clinical data and the fact, that predicting PA is difficult (!) 
+# 
+pout <- "drop" # Drop to first quartile
+
+# decl_rel
+# decl_abs
+# drop
+# hop
+
+
+## ====================================================================
+## Data
+## ====================================================================
+
+
+setwd("/Users/au301842/PhysicalActivityandStrokeOutcome/1 PA Decline/")
+
+source("data_set.R")
+# Loading data-set from USB, to not store on computer
+
+source("data_format.R")
+
+## ====================================================================
+# Libraries
+## ====================================================================
+
+
+library(tidyverse)
+library(glue)
+library(patchwork)
+# library(ggdendro)
+library(corrplot)
+library(gt)
+library(gtsummary)
+
+
+## ====================================================================
+## 
+## Baseline
+## 
+## ====================================================================
+
+
+## ====================================================================
+# Step 0: labels
+## ====================================================================
+
+
+lbs<-var.labels[match(colnames(X_tbl), 
+                      names(var.labels))]
+
+ls<-lapply(1:ncol(X_tbl),function(x){
+  as.formula(paste0(names(lbs)[x],"~","\"",lbs[x],"\""))
+})
+
+ts<-tbl_summary(X_tbl|>filter(pase_0_cut!="1"),
+                by = "group",
+                missing = "no",
+                # label = ls[-length(ls)], ## Removing the last, as this is output
+                value = list(where(is.factor) ~ "2"),
+                type = list(mrs_0 ~ "categorical"),
+                statistic = list(all_continuous() ~ "{median} ({p25};{p75}) [{min},{max}]")
+)%>%
+  add_overall() %>%
+  add_n()%>%
+  as_gt()
+
+ts
+
+ts_rtf <- file("table1.RTF", "w")
+writeLines(ts%>%as_rtf(), ts_rtf)
+close(ts_rtf)
+
+
+
+## ====================================================================
+# Step 1: labels
+## ====================================================================
+lbs<-var.labels[match(colnames(X_tbl_f), names(var.labels))]
+
+ls<-lapply(1:ncol(X_tbl_f),function(x){
+  as.formula(paste0(names(lbs)[x],"~","\"",lbs[x],"\""))
+})
+
+## ====================================================================
+# Step 2: table - edited
+## ====================================================================
+
+ts_e<-tbl_summary(X_tbl,
+                  missing = "no",
+                  value = list(where(is.factor) ~ "2"),
+                  type = list(mrs_0 ~ "categorical",
+                              mrs_1 ~ "categorical"),
+                  statistic = list(all_continuous() ~ "{median} ({p25};{p75}) [{min},{max}]")
+)%>%
+  as_gt()
+
+ts_e
+
+## ====================================================================
+# Step 3: table export
+## ====================================================================
+
+ts_rtf <- file("table1_overall.RTF", "w")
+writeLines(ts%>%as_rtf(), ts_rtf)
+close(ts_rtf)
+
+## ====================================================================
+# Baseline table - by PASE group
+## ====================================================================
+
+ts_q <- X_tbl |> 
+  select(vars) |> 
+  mutate(pase_0_cut = factor(quantile_cut(pase_0, groups = 4)[[1]],ordered = TRUE)) |> 
+  select(-pase_6,-pase_0) |> 
+  tbl_summary(missing = "no",
+                  by="pase_0_cut",
+                  value = list(where(is.factor) ~ "2"),
+                  type = list(mrs_0 ~ "categorical"),
+                  statistic = list(all_continuous() ~ "{median} ({p25};{p75}) [{min},{max}]")
+) |> 
+  add_overall() |> 
+  add_n ()
+
+ts_q
+
+## ====================================================================
+# Drops and hops
+## ====================================================================
+
+# TRUEs are patients dropping
+table(X_tbl$pase_0_cut!="1"&X_tbl$pase_6_cut=="1")/nrow(X_tbl[X_tbl$pase_0_cut!="1",])
+
+# TRUEs are percentage of patients inactive before stroke being more active after
+table(X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1")/nrow(X_tbl[X_tbl$pase_0_cut=="1",])
+
+# TRUEs are percentage of patients being more active after that were inactive before stroke
+table(X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1")/nrow(X_tbl[X_tbl$pase_6_cut!="1",])
+
+# Difference between hop/no-hop
+t.test(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"])
+
+summary(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1","pase_0"])
+
+summary(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"])
+
+boxplot(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"])
+
+# Stationary low
+t.test(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_6"])
+
+boxplot(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_6"])
+
+## ====================================================================
+# Sankey plot
+## ====================================================================
+
+if (pout=="drop"){
+  source("sankey.R")
+  p_delta
+}
+
+
+## ====================================================================
+# Six months PASE: Bivariate and multivariate analyses
+## ====================================================================
+
+dta_lmreg <- X_tbl |> 
+  select(vars) |> 
+  mutate(mrs_0=factor(ifelse(mrs_0==1,1,2))) 
+
+Hmisc::label(dta_lmreg$mrs_0) <- "Pre-stroke mRS >0"
+
+uv_reg <- tbl_uvregression(data=dta_lmreg, 
+                           method=lm, 
+                           y="pase_6",
+                           show_single_row = where(is.factor),
+                           estimate_fun = ~style_sigfig(.x,digits = 3),
+                           pvalue_fun = ~style_pvalue(.x, digits = 3)
+)
+
+mu_reg <- dta_lmreg |>
+  lm(formula=pase_6~.,data=_) |> 
+  tbl_regression(show_single_row = where(is.factor),
+                 estimate_fun = ~style_sigfig(.x,digits = 3),
+                 pvalue_fun = ~style_pvalue(.x, digits = 3)
+  )|> 
+  add_n()
+
+tbl_merge(list(uv_reg,mu_reg))
+
+
+## ====================================================================
+## 
+## Data variance
+## 
+## Illustrating principal components.
+## 
+## ====================================================================
+
+
+# source("PCA.R")
+# 
+# 
+# pca22
+# ggsave("pc_plot.png",width = 18, height = 12, dpi = 300, limitsize = TRUE, units = "cm")
+
+
+## ====================================================================
+## 
+## Models
+## 
+## ====================================================================
+
+
+source("assign_full.R")
+source("regularisation_steps.R")
+
+## ====================================================================
+# Step 1: data merge
+## ====================================================================
+tbl<-merge(reg_coef_tbl$'_data',full_coef_tbl$'_data',by="name",all.x=T, sort=F)
+
+## ====================================================================
+# Step 2: table
+## ====================================================================
+com_coef_tbl<-tbl%>%
+  gt()%>%
+  fmt_number(
+    columns=colnames(tbl)[sapply(tbl,is.numeric)], ## Selecting all numeric
+    rows = everything(),
+    decimals = 3)%>%
+  tab_spanner(
+    label = "Full model",
+    columns = 6:8
+  )%>%
+  tab_spanner(
+    label = paste0("Regularised model, (a=",
+                   best_alph,
+                   ", l=",
+                   round(best_lamb,3),
+                   ")"),
+    columns = 2:5
+  )%>%
+  tab_header(
+    title = "Model coefficients",
+    subtitle = "Combined table of both full and regularised model coefficients"
+  )
+
+com_coef_tbl
+
+## ====================================================================
+# Step 3: export
+## ====================================================================
+com_coef_rtf <- file("table2.RTF", "w")
+writeLines(com_coef_tbl%>%as_rtf(), com_coef_rtf)
+close(com_coef_rtf)
+
+
+
+## ====================================================================
+## 
+## Model performance
+## 
+## Table with performance meassures for the two different models.
+## 
+## ====================================================================
+
+# ROC curve of best model
+
+p_roc
+ggsave("roc_plot.png",width = 12, height = 12, dpi = 300, limitsize = TRUE, units = "cm")
+
+
+## ====================================================================
+# Step 1: data set
+## ====================================================================
+tbl<-data.frame(Meassure=c(names(full_cfm$byClass),"Mean AUC"),
+                "Regularised model"=round(c(reg_cfm$byClass,reg_auc_sum["Mean"]),3),
+                "Full model"=round(c(full_cfm$byClass,full_auc_sum["Mean"]),3))
+
+## ====================================================================
+# Step 2: table
+## ====================================================================
+tbl_perf<-tbl%>%
+  gt()%>%
+  tab_header(
+    title = "Performance meassures",
+    subtitle = "Combined table of both full and regularised performance meassures"
+  )
+
+tbl_perf
+
+## ====================================================================
+# Step 3: export
+## ====================================================================
+tbl_perf_rtf <- file("table3.RTF", "w")
+writeLines(tbl_perf%>%as_rtf(), tbl_perf_rtf)
+close(tbl_perf_rtf)
+# 
+
+## ====================================================================
+## 
+## Secondary analysis
+## 
+## ====================================================================
+
+Xy<-dta_s
+X<-dta_s|>select(-group)
+y<-dta_s$group
+
+
+source("assign_full.R")
+source("regularisation_steps.R")
+
+## ====================================================================
+# Step 1: data merge
+## ====================================================================
+tbl<-merge(reg_coef_tbl$'_data',full_coef_tbl$'_data',by="name",all.x=T, sort=F)
+
+## ====================================================================
+# Step 2: table
+## ====================================================================
+com_coef_tbl<-tbl%>%
+  gt()%>%
+  fmt_number(
+    columns=colnames(tbl)[sapply(tbl,is.numeric)], ## Selecting all numeric
+    rows = everything(),
+    decimals = 3)%>%
+  tab_spanner(
+    label = "Full model",
+    columns = 6:8
+  )%>%
+  tab_spanner(
+    label = paste0("Regularised model, (a=",
+                   best_alph,
+                   ", l=",
+                   round(best_lamb,3),
+                   ")"),
+    columns = 2:5
+  )%>%
+  tab_header(
+    title = "Model coefficients",
+    subtitle = "Combined table of both full and regularised model coefficients"
+  )
+
+com_coef_tbl
+
+## ====================================================================
+# Step 3: export
+## ====================================================================
+com_coef_rtf <- file("table2_sec.RTF", "w")
+writeLines(com_coef_tbl%>%as_rtf(), com_coef_rtf)
+close(com_coef_rtf)
+
+
+## ====================================================================
+## 
+## Model performance
+## 
+## Table with performance meassures for the two different models.
+## 
+## ====================================================================
+
+
+## ====================================================================
+# Step 1: data set
+## ====================================================================
+tbl<-data.frame(Meassure=c(names(full_cfm$byClass),"Mean AUC"),
+                "Regularised model"=round(c(reg_cfm$byClass,reg_auc_sum["Mean"]),3),
+                "Full model"=round(c(full_cfm$byClass,full_auc_sum["Mean"]),3))
+
+## ====================================================================
+# Step 2: table
+## ====================================================================
+tbl_perf<-tbl%>%
+  gt()%>%
+  tab_header(
+    title = "Performance meassures",
+    subtitle = "Combined table of both full and regularised performance meassures"
+  )
+
+tbl_perf
+
+## ====================================================================
+# Step 3: export
+## ====================================================================
+tbl_perf_rtf <- file("table3_sec.RTF", "w")
+writeLines(tbl_perf%>%as_rtf(), tbl_perf_rtf)
+close(tbl_perf_rtf)

1 PA Decline/archive/generation_1/PCA.R

@@ -0,0 +1,74 @@
+## ItMLiHSmar2022
+## PCA.R, child script
+## Principal components analysis for data visualisation
+## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk
+
+
+## ====================================================================
+## Step 0: data wrangling
+## ====================================================================
+# source("data_format.R")
+
+X1<-X %>% dplyr::mutate(across(where(is.factor), as.numeric))
+
+pc.out<-prcomp(X1, center=TRUE, scale = TRUE)
+
+pc.sum<-summary(pc.out)
+
+## ====================================================================
+## Step 1: plotting
+## ====================================================================
+Xy$group<-factor(Xy$group,labels = c("No decline", "Decline"))
+
+library(ggfortify)
+ppc12 <- autoplot(pc.out,
+                  data = Xy,
+                  x=1,
+                  y=2,
+                  colour = 'group')+
+  labs(title = "PC1 and PC2",
+       colour = "Outcome")
+ppc13 <- autoplot(pc.out,
+                  data = Xy,
+                  x=1,
+                  y=3,
+                  colour = 'group')+
+  labs(title = "PC1 and PC3",
+       colour = "Outcome")
+ppc23 <- autoplot(pc.out,
+                  data = Xy,
+                  x=2,
+                  y=3,
+                  colour = 'group')+
+  labs(title = "PC2 and PC3",
+       colour = "Outcome")
+
+# Scree plot
+pscr<-tibble(x=1:dim(pc.sum$importance)[2],
+             Proportion=pc.sum$importance[2,],
+             Cumulative=pc.sum$importance[3,])%>%
+  pivot_longer(cols=-x)%>%
+  ggplot(aes(x=x,y=value,color=name))+
+  geom_line()+
+  geom_point()+
+  ylim(0,1)+
+  labs(title = "Scree plot",
+       color= "Variance")+
+  ylab("Variance")+
+  xlab("Principal components")
+
+## ====================================================================
+## Step 2: merge plots
+## ====================================================================
+library(patchwork)
+pca22<-ppc12+
+  theme(legend.position="none")+
+  ppc13+
+  ppc23+theme(legend.position="none")+
+  pscr+ 
+  plot_layout(ncol=2)+
+  plot_annotation(title = 'Principal component visualisation',
+                  tag_levels = "A")
+
+# pca22
+

1 PA Decline/archive/generation_1/assign_full.R

@@ -0,0 +1,141 @@
+## ItMLiHSmar2022
+## assign_full.R, child script
+## Full model building and analysation for assignment
+## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk
+
+## ====================================================================
+## Step 0: data import and wrangling
+## ====================================================================
+
+# source("data_format.R")
+
+## ====================================================================
+## Step 1: settings
+## ====================================================================
+K<-10
+n<-nrow(X)
+set.seed(321)
+
+# Using caret function to ensure both levels represented in all folds
+c<-createFolds(y=y, k = K, list = FALSE, returnTrain = TRUE) 
+
+B<-list()
+auc_train<-auc_test<-c()
+
+catinfo<-levels(y)
+
+cMatTrain<-cMatTest<-table(factor(c(0,0),levels=catinfo),factor(c(0,0),levels=catinfo))
+
+## ====================================================================
+## Step 2: cross validation
+## ====================================================================
+
+set.seed(321)
+## Iterate over partitions
+for (idx1 in 1:K){
+  
+  # Status
+  cat('Processing fold', idx1, 'of', K,'\n')
+  
+  # idx1=1
+  # Get training- and test sets
+  I_train = c!=idx1 ## Creating selection vector of TRUE/FALSE
+  I_test = !I_train
+  
+  Xtrain = X[I_train,]
+  ytrain = y[I_train]
+  Xtest = X[I_test,]
+  ytest = y[I_test]
+  
+  # Z-score standardisation
+  source("standardise.R")
+  list2env(standardise(Xtrain,Xtest,type="cs"),.GlobalEnv) 
+  ## Outputs XtrainSt and XtestSt
+  ## Standardised by centering and scaling
+  
+  
+  ## Model matrices for glmnet
+  # Xmat.train<-model.matrix(~.-1,XtrainSt)
+  # Xmat.test<-model.matrix(~.-1,XtestSt)
+  
+  # Weights
+  ytrain_weight<-as.vector(1 - (table(ytrain)[ytrain] / length(ytrain)))
+  
+  # Fit regularized linear regression model
+  mod<-glm(ytrain~.,
+           data=XtrainSt,
+           weights = ytrain_weight,
+           family = stats::quasibinomial(link = "logit"))
+  
+  # Keep coefficients for plot
+  B[[idx1]] <- mod
+  
+  # Predict
+  yhatTrainProb<-predict(mod, 
+                         newdata = XtrainSt,
+                         type = "response"
+  )
+  
+  yhatTestProb<-predict(mod, 
+                        newdata = XtestSt,
+                        type = "response"
+  )
+  
+  # Compute training and test error
+  yhatTrain = round(yhatTrainProb)
+  yhatTest = round(yhatTestProb)
+  
+  # Make predictions categorical again (instead of 0/1 coding)
+  yhatTrainCat = factor(round(yhatTrainProb),levels=c("0","1"),labels=catinfo,ordered = TRUE)
+  yhatTestCat = factor(round(yhatTestProb),levels=c("0","1"),labels=catinfo,ordered = TRUE)
+  # Compute confusion matrices
+  cMatTrain = cMatTrain + table(ytrain,yhatTrainCat)
+  cMatTest = cMatTest + table(ytest,yhatTestCat)
+  
+  # AUROC
+  suppressMessages(
+    auc_train[idx1]<-auc(ytrain, yhatTrainCat))
+  suppressMessages(
+    auc_test [idx1]<-auc(ytest, yhatTestCat))
+}
+
+## ====================================================================
+# Step 3: creating table of coefficients for inference
+## ====================================================================
+
+confs<-lapply(1:K, function(x){
+  cs<-exp(confint(B[[x]]))
+  lo<-cs[,1]
+  hi<-cs[,2]
+  return(list(lo=lo,hi=hi))
+})
+
+coefs<-apply(Reduce('cbind',  lapply(B,"[[", "coefficients")),1,mean)
+
+unlist(strsplit(names(B[[1]]$coefficients),2))
+
+var.labels<-c(var.labels,'(Intercept)'="Intercept")
+
+ds<-tibble(name=var.labels[match(unlist(strsplit(names(coefs),2)), names(var.labels))],
+           coefs=coefs,
+           OR=round(exp(coefs),3),
+           CIs=paste0("(",
+                      round(apply(Reduce('cbind',  lapply(confs,"[[", "lo")),1,mean),3),
+                      ",",
+                      round(apply(Reduce('cbind',  lapply(confs,"[[", "hi")),1,mean),3),
+                      ")")
+           )
+
+# 
+full_coef_tbl<-ds%>%
+  gt(rowname_col = list(age~"Age"))
+# 
+full_coef_tbl
+
+## ====================================================================
+# Step 4: plotting classification performance
+## ====================================================================
+
+full_cfm<-confusionMatrix(cMatTest)
+full_auc_sum<-summary(auc_test)
+

1 PA Decline/archive/generation_1/assigndata.csv

@@ -0,0 +1,643 @@
+"pase_0","age","sex","civil","smoke_ever","rtreat","alc","afli","hypertension","diabetes","mrs_0","nihss_c","thrombolysis","pad","thrombechtomy","ami","tci","pase_drop","pase_6","mrs_1","mfi_gen_1","mdi_1","who5_score_1"
+"377.44","76","male","partner","never","Placebo","guideline","no","yes","no","0","2","yes","no","no","yes","no","no","260.52","0","10","6","84"
+"277","49","male","partner","never","Placebo","guideline","no","no","no","0","4","no","no","no","no","no","no","113.11","2","12","11","64"
+"192.4","43","male","alone","never","Placebo","guideline","no","yes","yes","0","2","yes","no","no","no","no","no","123.05","4","12","3","76"
+"30","89","female","alone","ever","Placebo","guideline","yes","no","no","0","3","no","no","no","no","no",NA,NA,NA,NA,NA,NA
+"44.14","80","male","partner","ever","Active","guideline","no","no","no","2","4","no","no","no","no","no","no","135.15","3","16","11","64"
+"128.76","72","male","partner","never","Placebo","guideline","no","yes","no","0","1","no","no","no","no","no",NA,NA,NA,NA,NA,NA
+"224.54","71","female","partner","ever","Placebo","guideline","no","yes","no","0","1","no","no","no","no","no","no","197.78","1","8","1","80"
+"100","66","female","alone","never","Active","guideline","no","no","no","0","4","no","no","yes","no","no","yes","32.36","4","17","14","52"
+"144.8","64","male","partner","never","Placebo","guideline","no","no","no","0","3","no","no","no","no","no","no","191.84","1","14","3","84"
+"136.8","64","male","partner","never","Placebo","guideline","no","yes","no","0","3","no","yes","no","no","no","yes","9.7","2","10","7","36"
+"134.33","65","female","alone","ever","Placebo","guideline","no","yes","yes","0","2","no","no","no","no","no","no","178.97","2","10","4","80"
+"118.2","63","male","partner","never","Active","guideline","no","yes","no","0","9","yes","no","no","no","no","yes","33.14","2","4","3","48"
+"99.28","62","male","partner","never","Active","more","no","yes","no","0","1","no","no","no","no","no","no","209.68","1","15","9","48"
+"101.3","73","male","partner","never","Placebo","more","no","no","no","1","16","yes","no","yes","no","no","yes","34.6","1","14","12","0"
+"75.5","59","female","alone","never","Placebo","guideline","no","no","no","0","5","no","no","no","no","no","no","123.97","2","20","22","44"
+"81","73","male","partner","never","Active","guideline","no","yes","no","0","2","no","no","no","yes","no","no","184.4","1","16","10","44"
+"79.08","70","male","partner","never","Active","guideline","yes","no","no","0","5","yes","no","no","no","no","no","235.75","2","17","9","60"
+"27.2","83","male","partner","never","Active","guideline","no","yes","no","0","3","no","no","no","no","no","no","81","0","6","3","68"
+"173.25","80","male","partner","ever","Active","guideline","no","yes","no","0","5","no","no","no","no","no",NA,NA,NA,NA,NA,NA
+"255.8","63","male","alone","never","Placebo","guideline","no","no","no","0","9","no","no","no","no","no","no","121.4","1","4","5","84"
+"27.2","82","male","alone","never","Active","guideline","no","yes","no","0","3","no","no","no","no","no",NA,NA,NA,NA,NA,NA
+"329.51","72","male","partner","ever","Placebo","guideline","yes","no","no","1","11","yes","no","no","no","no","no","506.35","2","11","2","80"
+"199.91","58","female","alone","ever","Active","guideline","no","yes","no","0","0","no","no","no","no","no",NA,NA,NA,NA,NA,NA
+"218.09","64","male","partner","ever","Active","guideline","no","no","no","0","2","no","no","no","no","no","no","117.25","0","4","1","92"
+"166.8","49","female","partner","ever","Placebo","guideline","no","no","no","0","5","no","no","no","no","no","no","183.58","0","5","6","76"
+"25","99","female","alone","ever","Placebo","guideline","yes","no","no","0","8","yes","no","no","no","no","no","8.82","2","12","14","52"
+"90","79","female","alone","never","Placebo","guideline","no","yes","no","0","2","yes","no","no","yes","yes","no","136.4","0","16","11","96"
+"232.71","60","male","partner","never","Active","guideline","no","yes","no","0","5","no","no","no","no","no","no","228.25","2","12","9","72"
+"208.24","51","male","alone","never","Placebo","guideline","no","no","no","0","10","no","no","no","no","no","no","98.14","2","12","4","76"
+"28.11","77","male","alone","never","Placebo",NA,"yes","yes","no","0","9","no","no","no","no","no","no","8.4","4","10","14","52"
+"116","54","female","partner","never","Active","guideline","no","yes","no","0","6","no","no","no","no","no","no","110.32","0",NA,NA,"0"
+"271.5","71","female","partner","ever","Placebo","guideline","no","yes","no","0","16","yes","no","no","no","no","no","288.88","3","13","10","72"
+"155.8","67","female","alone","never","Placebo","more","no","yes","no","0","1","no","yes","no","no","no","no","207.4","0","6","0","100"
+"116.4","31","male","alone","never","Active","guideline","no","no","no","0","5","no","no","no","no","no","no","81.32","1","4","5","92"
+"88.76","72","female","partner","never","Active","guideline","yes","yes","no","1","5","no","no","no","no","no","no","126.65","1","11","8","64"
+"78.48","71","female","partner","never","Active","guideline","no","no","no","1","15","yes","no","no","no","no","no","152.22","0","4","4","80"
+"98.68","75","female","partner","ever","Active","guideline","no","no","no","0","4","no","no","no","no","no","yes","60.83","2","9","3","84"
+"183.3","78","male","partner","never","Active","guideline","no","no","no","0","2","no","no","no","no","no","no","240.04","1","12","1","76"
+"192.5","74","male","partner","never","Active","guideline","no","no","no","0","3","yes","no","no","no","no",NA,NA,NA,NA,NA,NA
+"217.82","62","female","partner","ever","Placebo","guideline","no","yes","no","1","3","yes","no","no","no","no","no","185.18","1","11","6","64"
+"144.4","86","male","partner","ever","Active","guideline","no","yes","no","0","2","yes","no","no","no","no","no","171.37","1","5","5","84"
+"85","82","male","partner","never","Active","guideline","no","yes","no","0","3","yes","no","no","no","no","yes","62.8","0","20","18","48"
+"168.87","60","female","partner","never","Active","guideline","no","yes","no","0","0","no","no","no","no","no","no","230.14","2","15","14","40"
+"115.56","63","female","partner","never","Placebo","guideline","no","yes","no","0","2","no","no","no","no","no","no","188.73","2","13","12","60"
+"111","53","male","partner","never","Placebo","guideline","no","no","no","0","3","yes","no","no","no","no","no","136","1","17","6","92"
+"58.53","86","female","alone","never","Active","guideline","no","yes","no","0","0","no","no","no","no","no","no","246.47","0","14","4","52"
+"75.8","75","male","partner","never","Placebo","guideline","yes","no","yes","0","2","yes","no","no","no","no","no","108.2","2","11","14","64"
+"153.01","70","female","alone","never","Placebo","guideline","no","yes","no","0","2","no","no","no","no","no","no","228.24","2","7","3","88"
+"114.93","63","male","partner","never","Placebo","guideline","no","no","yes","0","3","no","no","no","no","no","no","188.05","2","18","5","72"
+"243.33","57","male","alone","never","Placebo",NA,"no","no","no","0","6","no","no","yes","no","no","no","199.86","3","7","5","8"
+"218.89","77","male","alone","ever","Placebo","guideline","no","no","no","0","3","no","no","no","no","no","yes","32.09","1","5","11","88"
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1 PA Decline/archive/generation_1/data_format.R

@@ -0,0 +1,290 @@
+## ItMLiHSmar2022
+## data_format.R, child script
+## Data formatting and handling
+## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk
+## 
+## Now modified to use in publication
+## 
+
+## ====================================================================
+# Step 1: Loading libraries
+## ====================================================================
+
+library(Hmisc)
+library(dplyr)
+library(daDoctoR)
+library(tidyselect)
+
+## ====================================================================
+# Step 2: Loading data
+## ====================================================================
+ 
+# rm(list = ls()) # Clear
+# setwd("/Users/au301842/Library/CloudStorage/OneDrive-Personligt/Research/ISLcourse/") 
+# dta<-read.csv("/Users/au301842/Library/CloudStorage/OneDrive-Personligt/Research/ISLcourse/assigndata.csv")
+
+## ====================================================================
+# Step 3: Formatting variables
+## ====================================================================
+
+dta <- export %>% 
+  # as_tibble()%>%
+  mutate(any_rep=factor(ifelse(thrombolysis=="yes"|thrombechtomy=="yes","yes","no")), # If not noted, no therapy was received
+         male_sex= factor(ifelse(sex=="female","no","yes")),
+         # smoke_ever=factor(ifelse(smoke_ever=="never","no","yes")),
+         civil=factor(ifelse(civil=="partner","no","yes")), # Sets "yes" for not-cohabiting
+         rtreat=factor(ifelse(rtreat=="Placebo","no","yes")), # "Yes" receives active treatment
+         alc=factor(ifelse(alc=="more","yes","no")), # Yes for more than guideline
+         pase_0=as.numeric(pase_0),
+         pase_6=as.numeric(pase_6),
+         across(c("diabetes",
+                  "hypertension",
+                  "smoker",
+                  # "smoker_prev",
+                  "afli",
+                  "pad",
+                  "ami",
+                  "tci",
+                  "mrs_0",
+                  "mrs_1"),as.factor),
+         across(c("nihss_c",
+                  "age",
+                  "mdi_1", # For "enriched" analysis
+                  "who5_score_1",
+                  "mfi_gen_1",
+                  "mfi_phys_1",
+                  "mfi_act_1",
+                  "mfi_mot_1",
+                  "mfi_men_1"),as.numeric )
+         )%>% 
+  select(-c(sex))
+  
+  
+## ====================================================================
+# Step 4: Defining outcome
+## ====================================================================
+
+## Changed to step 7
+## This is to perform proper quantile split based on actually included.
+
+## ====================================================================
+# Step 5: Ordering variables
+## ====================================================================
+
+vars <- c("age",
+          "male_sex",
+          "civil",
+          "pase_0",
+          "smoker",
+          "alc",
+          "afli",
+          "hypertension",
+          "diabetes",
+          "pad",
+          "ami",
+          "tci",
+          "mrs_0",
+          "nihss_c",
+          "any_rep",
+          "rtreat",
+          "pase_6")
+
+dta<-select(dta,c(vars, 
+                  "mrs_1",
+                  "mfi_gen_1",
+                  "mfi_phys_1",
+                  "mfi_act_1",
+                  "mfi_mot_1",
+                  "mfi_men_1",
+                  "mdi_1", 
+                  "who5_score_1"
+                  ))
+
+## ====================================================================
+# Step 6: Labeling
+## ====================================================================
+
+var.labels = c(age="Age",
+               male_sex="Male",
+               civil="Living alone",
+               pase_0="Pre-stroke PASE score",
+               pase_6="Six month PASE score",
+               smoker="Daily or occasinally smoking",
+               # smoker_prev="Previous habbit of smoking",
+               alc="More alcohol than recommendation",
+               afli="AFIB",
+               hypertension="Hypertension",
+               diabetes="Diabetes",
+               pad="PAD",
+               ami="Previous MI",
+               tci="Previous TIA",
+               mrs_0="Pre-stroke mRS  [-1]",
+               nihss_c="Acute NIHSS score",
+               thrombolysis="Acute thrombolysis",
+               thrombechtomy="Acute thrombechtomy",
+               any_rep="Any reperfusion therapy",
+               rtreat="Active trial treatment",
+               mrs_1="One month mRS [-1]",
+               mfi_gen_1="One month MFI (General fatigue)",
+               mfi_phys_1="One month MFI (Physical fatigue)",
+               mfi_act_1="One month MFI (Reduced activity)",
+               mfi_mot_1="One month MFI (Reduced motivation)",
+               mfi_men_1="One month MFI (Mental fatigue)",
+               mdi_1="One month MDI",
+               who5_score_1="One month WHO5",
+               pase_decl_rel_fac="PASE score difference, relative F",
+               pase_decl_abs_fac="PASE score difference, absolute F",
+               pase_drop_fac="PASE first quartile drop F",
+               pase_hop_fac="PASE first quartile hop F",
+               pase_diff="PASE absolute decline",
+               pase_decl_rel="PASE relative decline",
+               pase_0_cut="PASE 0 quartiles",
+               pase_6_cut="PASE 6 quartiles")
+
+## Labelling based on outcome flag
+if (pout=="decl_rel"|pout=="decl_abs"){
+  var.labels = c(var.labels,group="PASE decline")}
+if (pout=="drop"){
+  var.labels = c(var.labels,group="PASE drop")}
+
+## ====================================================================
+# Step 7: final data export
+## ====================================================================
+
+data_summary<-summary(dta)
+
+# Saving "old" factorised variables
+sel<-sapply(dta,is.factor)
+# Reformatting factors as 1/2 for analysis
+dta<-dta |> 
+  mutate(across(where(is.factor), as.numeric))|> # Turning factors into 1(no) or 2(yes) for model. Numbered alphabetically.
+  mutate(across(matches(colnames(dta)[sel]), as.factor),
+         across(starts_with("pase_"), as.numeric))
+
+# Filtering out non-PASE
+X_tbl<-dta |>
+  filter(!is.na(pase_0),!is.na(pase_6))
+
+nrow(X_tbl)
+
+# Defining possible outcome meassures. Keeping in df for characterisation
+X_tbl <- X_tbl|>
+  mutate(## Relative decline
+         pase_diff=(pase_0-pase_6),
+         pase_decl_rel = pase_diff/pase_0*100,
+         pase_decl_rel_fac=factor(ifelse(pase_decl_rel>=rel_dif,"yes","no")),
+         ## Absolute decline
+         pase_decl_abs_fac=factor(ifelse(pase_diff>=abs_dif,"yes","no")),
+         ## Drop
+         pase_0_cut=quantile_cut(as.numeric(pase_0),
+                                 groups=4,
+                                 group.names = c(as.character(1:4)),
+                                 y=as.numeric(pase_0),
+                                 ordered.f = TRUE,
+                                 inc.outs = TRUE, 
+                                 detail.lst=FALSE),
+         pase_6_cut=quantile_cut(as.numeric(pase_6),
+                                 groups=4,
+                                 group.names = c(as.character(1:4)),
+                                 y=as.numeric(pase_0),
+                                 ordered.f = TRUE,
+                                 inc.outs = TRUE, 
+                                 detail.lst=FALSE),
+         pase_drop_fac=factor(ifelse(pase_6_cut==1&pase_0_cut!=1,"yes","no")),
+         pase_hop_fac=factor(ifelse(pase_6_cut!=1&pase_0_cut==1,"yes","no")))
+
+Hmisc::label(X_tbl) = as.list(var.labels[match(names(X_tbl), names(var.labels))])
+
+# Setting final primary output from "pout"
+if (pout=="decl_rel"){
+  X_tbl <- X_tbl|>
+    mutate(group=pase_decl_rel_fac)
+  
+  X_tbl_f <- X_tbl|>
+    filter(pase_0!=0)|>
+    select(-starts_with("pase_"))
+}
+
+if (pout=="decl_abs"){
+  X_tbl <- X_tbl|>
+    mutate(group=pase_decl_rel_fac)
+  
+  X_tbl_f <- X_tbl|>
+    filter(pase_0>=abs_dif)|>
+    select(-starts_with("pase_"))
+}
+
+if (pout=="drop"){
+  X_tbl <- X_tbl|>
+    mutate(group=pase_drop_fac)
+  
+  # print(quantile(as.numeric(X_tbl$pase_0)))
+  # print(quantile(as.numeric(X_tbl$pase_6)))
+  # print(summary(X_tbl$pase_0_cut))
+  
+  X_tbl_f <- X_tbl|>
+    filter(pase_0_cut!=1)|>
+    select(-starts_with("pase_"))
+}
+
+if (pout=="hop"){
+  X_tbl <- X_tbl|>
+    mutate(group=pase_hop_fac)
+  
+  # print(quantile(as.numeric(X_tbl$pase_0)))
+  # print(quantile(as.numeric(X_tbl$pase_6)))
+  # print(summary(X_tbl$pase_0_cut))
+  
+  X_tbl_f <- X_tbl|>
+    filter(pase_6_cut!=1)|>
+    select(-starts_with("pase_"))
+}
+
+# Excluding one month measures for primary analysis and setting df for table one
+X_tbl_f <- X_tbl_f |> 
+  select(-c(who5_score_1,
+            mdi_1,
+            mrs_1,
+            starts_with("mfi_"))) # Left out of model as no present in drop-group
+
+# Dropping non-complete for analysis
+Xy <- X_tbl_f|> 
+  na.omit()|> # Keeping only complete observations
+  select(-c(tci) # Left out of model as no present in drop-group
+  )|>
+  mutate(mrs_0=factor(ifelse(mrs_0==1,1,2))) # Sets binary mRS 0 to include in glmnet, 0 or above
+
+label(Xy) = as.list(var.labels[match(names(Xy), names(var.labels))])
+
+X<-dplyr::select(Xy,-c(group, -starts_with("pase_")) # Exclude primary outcome
+                 )
+y<-Xy$group
+
+
+## ====================================================================
+# Secondary analysis
+## ====================================================================
+
+
+dta_s<-X_tbl|>
+  select(-c(tci),
+         -starts_with("pase_"))|> 
+  na.omit()|>
+  mutate(mrs_0=factor(ifelse(mrs_0==1,1,2)),# Sets binary mRS 0 to include in glmnet, 0 or above
+         mrs_1=factor(ifelse(mrs_1==1,1,2)))# Sets binary mRS 1 to include in glmnet, 0 or above
+
+label(dta_s) = as.list(var.labels[match(names(dta_s), names(var.labels))])
+
+## ====================================================================
+# Step 8: Loading rest of libraries
+## ====================================================================
+
+library(tidyverse)
+library(patchwork)
+library(caret)
+library(glmnet)
+library(leaps)
+library(pROC)
+library(gt)
+library(gtsummary)
+library(dplyr)

1 PA Decline/archive/generation_1/data_set.R

@@ -0,0 +1,49 @@
+## ItMLiHS assignment data set
+
+export<-read.csv("/Volumes/Data 1/exercise/source/background.csv",colClasses = "character", na.strings = c("NA","","unknown"))
+
+export<-export[,c("pase_0",
+               "age",
+               "sex",
+               "civil",
+               "smoke_ever", 
+               "smoker",
+               "rtreat",
+               "alc",
+               "afli",
+               "hypertension",
+               "diabetes",
+               "mrs_0",
+               "nihss_c",
+               "thrombolysis",
+               "pad", 
+               "thrombechtomy", 
+               "ami", 
+               "tci", 
+               "pase_6", 
+               "mrs_1", 
+               "mfi_gen_1", 
+               "mfi_phys_1", 
+               "mfi_act_1", 
+               "mfi_mot_1", 
+               "mfi_men_1",
+               "mdi_1",
+               "who5_score_1")]
+
+export$diabetes[is.na(export$diabetes)]<-"no"
+export$diabetes[is.na(export$hypertension)]<-"no"
+export$thrombolysis[is.na(export$thrombolysis)]<-"no"
+export$thrombechtomy[is.na(export$thrombechtomy)]<-"no"
+export$pad[is.na(export$pad)]<-"no"
+export$ami[is.na(export$ami)]<-"no"
+# export$smoker_prev <- ifelse(export$smoker=="3","yes","no")
+export$smoker <- ifelse(export$smoker=="1","yes","no")
+export$smoker[is.na(export$smoker)] <- "no"
+# export$mrs_0[export$mrs_0==3]<-NA
+
+
+
+# export<-na.omit(export)
+
+export
+# write.csv(export,"/Users/au301842/Library/CloudStorage/OneDrive-Personligt/Research/ISLcourse/assigndata.csv",row.names = FALSE)

1 PA Decline/archive/generation_1/dataset_redcap.R

@@ -0,0 +1,138 @@
+# Data
+## Import from previous work
+# dta<-read.csv("/Volumes/Data/exercise/source/background.csv",na.strings = c("NA","","unknown"),colClasses = "character")
+
+library(REDCapR)
+library(lubridate)
+library(dplyr)
+library(daDoctoR)
+
+# source("https://raw.githubusercontent.com/agdamsbo/daDoctoR/master/R/dob_extract_cpr_function.R")
+
+dta <- redcap_read_oneshot(
+  redcap_uri   = "https://redcap.au.dk/api/",
+  token        = read.csv("/Users/au301842/talos_redcap_token.csv",header = FALSE)[[1]],
+  fields       = c("talos_basis02a", #Indlæggelsesdato
+                   "cpr",
+                   "talos_nihss16_0", #Akut NIHSS
+                   "basis_kon", 
+                   "reg_hojde",  #Alle fra "reg(ister/DAP)"
+                   "reg_vaegt", 
+                   "reg_vaegt_anslaaet", 
+                   "reg_rygning", 
+                   "reg_alkohol", 
+                   "reg_civil", 
+                   "reg_bolig", 
+                   "reg_diabetes", 
+                   "reg_hyperten", 
+                   "reg_perifer_arteriel", 
+                   "reg_atriefli", 
+                   "reg_ami", 
+                   "reg_tidl_tci", 
+                   "reg_trombolyse", 
+                   "reg_trombektomi",
+                   "rtreat" #Trial treatment
+                   )
+)$data |>
+  mutate(age=time_length(talos_basis02a-dob_extract_cpr(cpr),
+                         unit="year")
+         )|>
+  select(!c("cpr"))
+
+
+
+
+## Cleaning and enhancing
+dta$pase_drop<-factor(ifelse((dta$pase_0_q=="q_2"|dta$pase_0_q=="q_3"|dta$pase_0_q=="q_4")&dta$pase_06_q=="q_1","yes","no"),levels = c("no","yes"))
+dta$pase_drop[is.na(dta$pase_6)]<-NA
+dta$pase_drop[is.na(dta$pase_0)]<-NA
+
+## Selection of data set and formatting
+library(dplyr)
+dta_f<-dta %>% filter(pase_0_q != "q_1" & !is.na(pase_drop))
+
+
+variable_names<-c("age","sex","weight","height",
+                     "bmi",
+                     "smoke_ever",
+                     "civil",
+                     "diabetes", 
+                     "hypertension",
+                     "pad", 
+                     "afli",  
+                     "ami", 
+                     "tci",
+                     "nihss_0",
+                     "thrombolysis", 
+                     "thrombechtomy",
+                     "rep_any","pase_0_q","pase_drop")
+
+
+library(daDoctoR)
+dta2<-dta_f[,variable_names]
+
+dta2<-col_num(c("age","weight","height","bmi","nihss_0"),dta2)
+dta2<-col_fact(c("sex","smoke_ever","civil","diabetes", "hypertension","pad", "afli",  "ami", "tci","thrombolysis", "thrombechtomy","rep_any","pase_0_q","pase_drop"),dta2)
+
+## Partitioning
+library(caret)
+set.seed(100)
+
+## Step 1: Get row numbers for the training data
+trainRowNumbers <- createDataPartition(dta2$pase_drop, p=0.8, list=FALSE)
+
+## Step 2: Create the training  dataset
+trainData <- dta2[trainRowNumbers,]
+
+## Step 3: Create the test dataset
+testData <- dta2[-trainRowNumbers,]
+y_test = testData[,"pase_drop"]
+
+# Store X and Y for later use.
+x = trainData %>% select(!matches("pase_drop"))
+y = trainData[,"pase_drop"]
+
+# Normalization and dummy binaries
+
+# One-Hot Encoding
+# Creating dummy variables is converting a categorical variable to as many binary variables as here are categories.
+dummies_model <- dummyVars(pase_drop ~ ., data=trainData)
+
+# Create the dummy variables using predict. The Y variable (Purchase) will not be present in trainData_mat.
+trainData_mat <- predict(dummies_model, newdata = trainData)
+
+# # Convert to dataframe
+trainData <- data.frame(trainData_mat)
+
+# # See the structure of the new dataset
+str(trainData)
+
+dummies_model <- dummyVars(pase_drop ~ ., data=testData)
+testData_mat <- predict(dummies_model, newdata = testData)
+testData <- data.frame(testData_mat)
+preProcess_range_model <- preProcess(testData, method='range')
+testData <- predict(preProcess_range_model, newdata = testData)
+testData$pase_drop<-y_test
+
+# Imputation
+
+library(RANN)  # required for knnInpute
+preProcess_missingdata_model <- preProcess(trainData, method='knnImpute')
+# preProcess_missingdata_model
+
+trainData <- predict(preProcess_missingdata_model, newdata = trainData)  # Giver fejl??
+anyNA(trainData)
+
+# skimr::skim(trainData)
+# skimr::skim(x)
+
+preProcess_range_model <- preProcess(trainData, method='range')
+trainData <- predict(preProcess_range_model, newdata = trainData)
+
+# Append the Y variable
+trainData$pase_drop <- y
+
+
+# Export
+write.csv(trainData,"/Users/au301842/PhysicalActivityandStrokeOutcome/data/trainData.csv",row.names = FALSE)
+write.csv(testData,"/Users/au301842/PhysicalActivityandStrokeOutcome/data/testData.csv",row.names = FALSE)

1 PA Decline/archive/generation_1/regular_fun.R

@@ -0,0 +1,117 @@
+## ItMLiHSmar2022
+## regular_fun.R, child script
+## Regularisation model building function
+## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk
+## 
+## Now modified to use in publication
+## 
+
+regular_fun<-function(X,y,K,lambdas,alpha){
+n<-nrow(X)
+set.seed(321)
+
+# Using caret function to ensure both levels represented in all folds
+c<-createFolds(y=y, k = K, list = FALSE, returnTrain = TRUE) 
+
+B<-yhatTestProbKeep<-list()
+accTrain<-accTest<-err_train<-err_test<-auc_train<-auc_test<-matrix(nrow = K,ncol = length(lambdas))
+
+catinfo<-levels(y)
+
+cMatTrain<-cMatTest<-table(true=factor(c(0,0),levels=catinfo),pred=factor(c(0,0),levels=catinfo))
+
+
+## Iterate over partitions
+for (idx1 in 1:K){
+  
+  # Status
+  cat('Processing fold', idx1, 'of', K,'\n')
+  
+  # idx1=1
+  # Get training- and test sets
+  I_train = c!=idx1 ## Creating selection vector of TRUE/FALSE
+  I_test = !I_train
+  
+  Xtrain = X[I_train,]
+  ytrain = y[I_train]
+  Xtest = X[I_test,]
+  ytest = y[I_test]
+  
+  
+  ## Model matrices for glmnet
+  ## Using the complicated approach not to include first level.
+  # Xmat.train<-model.matrix(~ .-1, data=Xtrain, 
+  #              contrasts.arg = lapply(Xtrain[,sapply(Xtrain, is.factor)], 
+  #                                     contrasts, contrasts=T))
+  # Xmat.test<-model.matrix(~ .-1, data=Xtest, 
+  #                contrasts.arg = lapply(Xtest[,sapply(Xtest, is.factor)], 
+  #                                       contrasts, contrasts=T))
+  
+  # Xmat.train<-model.matrix(~.-1,Xtrain)
+  # Xmat.test<-model.matrix(~.-1,Xtest)
+  
+  # Weights
+  ytrain_weight<-as.vector(1 - (table(ytrain)[ytrain] / length(ytrain)))
+  # ytest_weight<-as.vector(1 / (table(ytest)[ytest] / length(ytest)))
+  
+  # Fit regularized linear regression model
+  mod<-glmnet(Xtrain, ytrain, 
+              alpha = alpha,        ## Alpha = 1 for lasso
+              lambda = lambdas, ## Setting lambdas
+              standardize = TRUE, ## Scales and centers
+              weights = ytrain_weight,
+              family = "binomial"
+              )
+  
+  # Keep coefficients for plot
+  B[[idx1]] <- as.matrix(coef(mod))
+  
+  # Iterate over regularization strengths to compute training- and test
+  # errors for individual regularization strengths.
+  for (idx2 in 1:length(lambdas)){
+    # idx2=1
+    
+    # Predict
+    yhatTrainProb<-predict(mod, 
+                           s = lambdas[idx2], 
+                           newx = data.matrix(Xtrain),
+                           type = "response"
+    )
+    
+    yhatTestProb<-predict(mod, 
+                          s = lambdas[idx2], 
+                          newx = data.matrix(Xtest),
+                          type = "response"
+    )
+    
+    # Compute training and test error
+    yhatTrain = round(yhatTrainProb)
+    yhatTest = round(yhatTestProb)
+    
+    # Make predictions categorical again (instead of 0/1 coding)
+    yhatTrainCat = factor(round(yhatTrainProb),levels=c("0","1"),labels=catinfo,ordered = TRUE)
+    yhatTestCat = factor(round(yhatTestProb),levels=c("0","1"),labels=catinfo,ordered = TRUE)
+    
+    # Evaluate classifier performance
+    # Accuracy
+    # accTrain[idx1,idx2] <- sum(yhatTrainCat==ytrain)/length(ytrain)
+    # accTest [idx1,idx2] <- sum(yhatTestCat==ytest)/length(ytest)
+    # #
+    # # Error rate
+    # err_train[idx1,idx2] =  1 - accTrain[idx1,idx2]
+    # err_test [idx1,idx2] =   1 - accTest[idx1,idx2]
+    
+    # AUROC
+    suppressMessages(
+      auc_train[idx1,idx2]<-auc(ytrain, yhatTrainCat))
+    suppressMessages(
+      auc_test [idx1,idx2]<-auc(ytest, yhatTestCat))
+    
+    # Compute confusion matrices
+    cMatTrain = cMatTrain + table(true=ytrain,pred=yhatTrainCat)
+    cMatTest = cMatTest + table(true=ytest,pred=yhatTestCat)
+  }
+}
+ls<-list(mod=mod,B=B,auc_train=auc_train,auc_test=auc_test,cMatTrain=cMatTrain,cMatTest=cMatTest)
+return(ls)
+}

1 PA Decline/archive/generation_1/regularisation_steps.R

@@ -0,0 +1,142 @@
+## ItMLiHSmar2022
+## regularisation_steps.R, child script
+## Regularised model building and analysation for assignment
+## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk
+## 
+## Now modified to use in publication
+## 
+
+## ====================================================================
+## Step 0: data import and wrangling
+## ====================================================================
+
+setwd("/Users/au301842/PhysicalActivityandStrokeOutcome/1 PA Decline/")
+
+# source("data_format.R")
+y1<-factor(as.integer(y)-1) ## Outcome is required to be factor of 0 or 1.
+
+
+## ====================================================================
+## Step 1: settings
+## ====================================================================
+
+## Folds
+K=10
+set.seed(3)
+c<-caret::createFolds(y=y, 
+                      k = K, 
+                      list = FALSE, 
+                      returnTrain = TRUE) # Foldids for alpha tuning
+
+## Defining tuning parameters
+lambdas=2^seq(-10, 5, 1)
+alphas<-seq(0,1,.1)
+
+## Weights for models
+weighted=TRUE
+if (weighted == TRUE) {
+  wght<-as.vector(1 - (table(y)[y] / length(y)))
+} else {
+  wght <- rep(1, nrow(y))
+}
+
+
+## Standardise numeric
+## Centered and 
+
+
+
+## ====================================================================
+## Step 2: all cross validations for each alpha
+## ====================================================================
+
+library(furrr)
+library(purrr)
+library(doMC)
+registerDoMC(cores=6)
+
+# Nested CVs with analysis for all lambdas for each alpha
+# 
+set.seed(3)
+cvs <- future_map(alphas, function(a){
+  cv.glmnet(model.matrix(~.-1,X),
+            y1,
+            weights = wght,
+            lambda=lambdas, 
+            type.measure = "deviance", # This is standard measure and recommended for tuning
+            foldid = c, # Per recommendation the folds are kept for alpha optimisation
+            alpha=a,
+            standardize=TRUE,
+            family=quasibinomial,
+            keep=TRUE) # Same as binomial, but not as picky
+})
+
+## ====================================================================
+# Step 3: optimum lambda for each alpha
+## ====================================================================
+
+
+# For each alpha, lambda is chosen for the lowest meassure (deviance)
+each_alpha <- sapply(seq_along(alphas), function(id) {
+  each_cv <- cvs[[id]]
+  alpha_val <- alphas[id]
+  index_lmin <- match(each_cv$lambda.min, 
+                      each_cv$lambda)
+  c(lamb = each_cv$lambda.min, 
+    alph = alpha_val,
+    cvm = each_cv$cvm[index_lmin])
+})
+
+# Best lambda
+best_lamb <- min(each_alpha["lamb", ])
+
+# Alpha is chosen for best lambda with lowest model deviance, each_alpha["cvm",]
+best_alph <- each_alpha["alph",][each_alpha["cvm",]==min(each_alpha["cvm",]
+                                                         [each_alpha["lamb",] %in% best_lamb])]
+
+## https://stackoverflow.com/questions/42007313/plot-an-roc-curve-in-r-with-ggplot2
+p_roc<-roc.glmnet(cvs[[1]]$fit.preval, newy = y)[[match(best_alph,alphas)]]|> # Plots performance from model with best alpha
+  ggplot(aes(FPR,TPR)) + 
+  geom_step() +
+  coord_cartesian(xlim=c(0,1), ylim=c(0,1)) +
+  geom_abline()+
+  theme_bw()
+
+## ====================================================================
+# Step 4: Creating the final model
+## ====================================================================
+
+source("regular_fun.R") # Custom function
+optimised_model<-regular_fun(X,y1,K,lambdas=best_lamb,alpha=best_alph) 
+# With lambda and alpha specified, the function is just a k-fold cross-validation wrapper, 
+# but keeps model performance figures from each fold.
+
+list2env(optimised_model,.GlobalEnv)
+# Function outputs a list, which is unwrapped to Env.
+# See source script for reference.
+
+## ====================================================================
+# Step 5: creating table of coefficients for inference
+## ====================================================================
+
+Bmatrix<-matrix(unlist(B),ncol=10)
+Bmedian<-apply(Bmatrix,1,median)
+Bmean<-apply(Bmatrix,1,mean)
+
+reg_coef_tbl<-tibble(
+  name = c("Intercept",Hmisc::label(X)),
+  medianX = round(Bmedian,5),
+  ORmed = round(exp(Bmedian),5),
+  meanX = round(Bmean,5),
+  ORmea = round(exp(Bmean),5))%>%
+  # arrange(desc(abs(medianX)))%>%
+  gt()
+
+reg_coef_tbl
+
+## ====================================================================
+# Step 6: plotting predictive performance
+## ====================================================================
+
+reg_cfm<-confusionMatrix(cMatTest)
+reg_auc_sum<-summary(auc_test[,1])

1 PA Decline/archive/generation_1/sankey.R

@@ -0,0 +1,72 @@
+#
+# Sankey plot of quartile movement for drops
+#
+
+gth<-X_tbl[,c("pase_0_cut","pase_6_cut","pase_drop_fac")]
+
+gth$pase_drop_fac <- factor(ifelse(gth$pase_0_cut=="1",
+                                       "low",
+                                       gth$pase_drop_fac),
+                                labels = c("no","yes","low")) # Tried and tried to do vectorised, but failed. Matrices acting up..
+
+# Visuals - sankey
+# https://stackoverflow.com/questions/50395027/beautifying-sankey-alluvial-visualization-using-r
+
+
+## Painting
+# LOOK AT THIS GREAT FUNCTION!! Wide pivot format. Includes factor for possible quartile-colouring.
+
+df<-data.frame(gth %>% count(pase_0_cut,pase_6_cut,pase_drop_fac))
+
+lbs0<-c(paste0("1st\n(n=",sum(df$n[df[1]=="1"]),")"), 
+        paste0("2nd\n(n=",sum(df$n[df[1]=="2"]),")"), 
+        paste0("3rd\n(n=",sum(df$n[df[1]=="3"]),")"), 
+        paste0("4th\n(n=",sum(df$n[df[1]=="4"]),")"))
+
+lbs6<-c(paste0("1st\n(n=",sum(df$n[df[2]=="1"]),")"), 
+        paste0("2nd\n(n=",sum(df$n[df[2]=="2"]),")"), 
+        paste0("3rd\n(n=",sum(df$n[df[2]=="3"]),")"), 
+        paste0("4th\n(n=",sum(df$n[df[2]=="4"]),")"))
+
+df[1:2] <- as_factor(df[1:2])
+
+levels(df[,1])<-lbs0[1:length(levels(df[,1]))]
+levels(df[,2])<-lbs6[1:length(levels(df[,2]))]
+
+df[,3]<-factor(df[,3],levels=c("low","no","yes"))
+
+
+lows  <- "grey80"  # grey
+drops <- "#990033"  # Midtrød
+nos   <- "grey50" 
+nas   <- "grey90" 
+border<- "#66c1a3"
+box   <- "#7fccb2"
+
+cls <- c(lows,nos,drops)
+alpha <- 0.7
+
+library(ggalluvial)
+(p_delta<-ggplot(df,aes(y = n, axis1 = pase_0_cut, axis2 = pase_6_cut)) +
+  geom_alluvium(aes(fill = pase_drop_fac, color=pase_drop_fac), width = 1/10, alpha = alpha, knot.pos = 0.3)+
+  geom_stratum(width = 1/6, fill = box, color = border) +
+  geom_text(stat = "stratum", aes(label=after_stat(stratum))) +
+  scale_x_continuous(breaks = 1:2, labels = c("Pre-stroke\nPASE score\nquartiles", "Six months\nPASE score\nquartiles"))  +
+  scale_fill_manual(values  = cls) +
+  scale_color_manual(values = cls) +
+    scale_y_reverse() + # Easy solution to flip y-axis
+  labs(title="Change in physical activity") +
+    ylab("Quartiles")+
+  theme_minimal() +
+  theme(legend.position = "none", 
+        panel.grid.major = element_blank(), 
+        panel.grid.minor = element_blank(), 
+        axis.text.y = element_blank(),
+        axis.title.y = element_blank(),
+        axis.text.x = element_text(size = 14, face = "bold"), 
+        plot.title = element_text(hjust = 0.5, size = 20, face = "bold")))
+
+ggsave("sankey.png", plot = last_plot(), device = NULL, path = NULL,
+       scale = 1, width = 120, height = 200, dpi = 450, limitsize = TRUE,
+       units = "mm")
+          

1 PA Decline/archive/generation_1/standardise.R

@@ -0,0 +1,41 @@
+## ItMLiHSmar2022
+## standardise.R, child script
+## Data standardisation, returns list
+## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk
+
+standardise<-function(train,test,type){
+  # From:
+  # https://datascience.stackexchange.com/questions/13971/standardization-normalization-test-data-in-r
+  
+  sel<-sapply(Xtrain,is.numeric) # Deciding which to stadardise (only numeric)
+  cnm<-colnames(Xtrain) # Saving column names for ordering
+  
+  # Subsetting
+  
+  ## Data to treat
+  train.tr<-train[,sel]
+  test.tr<-test[,sel]
+  
+  ## Data to save
+  train.sv<-train[,!sel]
+  test.sv<-test[,!sel]
+  
+  # Calculate mean and SD of train data
+  trainMean <- sapply(train.tr,mean)
+  trainSd <- sapply(train.tr,sd)
+  
+  if (type=="c"){
+    ## centered
+    norm.trainData<-sweep(train.tr, 2L, trainMean) # using the default "-" to subtract mean column-wise   
+    norm.testData<-sweep(test.tr, 2L, trainMean) # using the default "-" to subtract mean column-wise   
+  }
+ 
+  if (type=="cs"){
+    ## centered AND scaled (Z-score standardisation)
+    norm.trainData<-sweep(sweep(train.tr, 2L, trainMean), 2, trainSd, "/")
+    norm.testData<-sweep(sweep(test.tr, 2L, trainMean), 2, trainSd, "/")
+  }
+  return(list(XtrainSt=cbind(norm.trainData,train.sv)[,cnm], # Reordering columns to original
+              XtestSt=cbind(norm.testData,test.sv)[,cnm]))
+}
+

1 PA Decline/archive/generation_1/table1.RTF

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+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 386}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 10 (2.6%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 8 (2.6%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 2 (2.7%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 Pre-stroke mRS  [-1]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 }}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 }}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 }}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 1}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 }}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 348 (89%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 284 (90%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 64 (83%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 2}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 }}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 32 (8.2%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 25 (8.0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 7 (9.1%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 3}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 }}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 10 (2.6%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 5 (1.6%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 5 (6.5%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 4}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 }}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 1 (0.3%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 1 (1.3%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 Acute NIHSS score}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 388}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 3.0 (2.0;5.0) [0.0,32.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 3.0 (2.0;5.0) [0.0,32.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 3.0 (2.0;7.0) [0.0,22.0]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 Any reperfusion therapy}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 151 (39%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 123 (39%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 28 (36%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 Active trial treatment}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 190 (49%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 147 (47%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 43 (56%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 Six month PASE score}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 159 (101;225) [0,486]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 180 (135;240) [86,486]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 57 (34;68) [0,83]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 One month mRS [-1]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 390}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 157 (40%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 136 (43%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 21 (27%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 One month MFI (General fatigue)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 378}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 10.0 (7.0;13.0) [4.0,20.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 10.0 (7.0;13.0) [4.0,20.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 12.0 (8.0;15.2) [4.0,20.0]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 One month MFI (Physical fatigue)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 376}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 10.0 (7.0;14.0) [4.0,20.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 9.0 (7.0;13.0) [4.0,20.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 13.0 (7.8;17.0) [4.0,20.0]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 One month MFI (Reduced activity)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 377}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 10.0 (7.0;13.0) [4.0,20.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 9.0 (6.0;12.0) [4.0,20.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 12.0 (9.0;16.0) [4.0,20.0]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 One month MFI (Reduced motivation)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 378}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 7.00 (5.00;9.00) [4.00,20.00]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 7.00 (5.00;9.00) [4.00,16.00]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 8.00 (5.00;12.00) [4.00,20.00]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 One month MFI (Mental fatigue)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 373}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 7.0 (4.0;11.0) [4.0,20.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 7.0 (4.0;10.0) [4.0,20.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 8.0 (5.0;12.0) [4.0,20.0]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 One month MDI}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 381}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 5.0 (3.0;9.0) [0.0,45.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 5.0 (2.0;8.0) [0.0,37.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 8.0 (4.0;15.0) [0.0,45.0]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 One month WHO5}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 385}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 76 (64;88) [0,100]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 76 (64;88) [0,100]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 70 (48;88) [0,100]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 PASE absolute decline}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 19 (-43;69) [-272,407]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 1 (-55;38) [-272,349]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 77 (45;127) [7,407]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 PASE relative decline}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 10 (-25;38) [-221,100]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 1 (-34;22) [-221,68]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 60 (41;75) [8,100]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 PASE score difference, relative F}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 PASE score difference, absolute F}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 PASE 0 quartiles}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 130 (33%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 83 (26%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 47 (61%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 PASE 6 quartiles}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 82 (21%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 82 (26%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 PASE first quartile drop F}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 PASE first quartile hop F}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 {\super \i 1}Median (25%;75%) [Minimum,Maximum]; n (%)}}\cell
+
+\row
+
+}

1 PA Decline/archive/generation_1/table1_overall.RTF

@@ -0,0 +1,737 @@
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+
+\paperw12240\paperh15840\widowctrl\ftnbj\fet0\sectd\linex0
+\lndscpsxn
+\margl1440\margr1440\margt1440\margb1440
+\headery720\footery720\fs20
+
+\trowd\trrh0\trhdr
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf1 \cellx1872
+\intbl {\f0 {\f0\fs20 {\b Characteristic}}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf1 \cellx3744
+\intbl {\f0 {\f0\fs20 {\b N}}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf1 \cellx5616
+\intbl {\f0 {\f0\fs20 {\b Overall}, N = 391{\super \i 1}}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf1 \cellx7488
+\intbl {\f0 {\f0\fs20 {\b no}, N = 314{\super \i 1}}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf1 \cellx9360
+\intbl {\f0 {\f0\fs20 {\b yes}, N = 77{\super \i 1}}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 Age}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 66 (57;74) [24,90]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 64 (56;72) [24,87]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 72 (66;79) [31,90]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 Male}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 275 (70%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 227 (72%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 48 (62%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 Living alone}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 385}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 107 (28%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 70 (23%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 37 (49%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 Pre-stroke PASE score}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 167 (122;225) [85,574]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 174 (135;231) [85,574]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 123 (103;184) [85,407]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 Daily or occasinally smoking}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 119 (30%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 90 (29%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 29 (38%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 More alcohol than recommendation}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 381}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 34 (8.9%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 25 (8.2%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 9 (12%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 AFIB}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 386}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 57 (15%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 45 (15%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 12 (16%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 Hypertension}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 387}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 184 (48%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 139 (45%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 45 (59%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 Diabetes}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 37 (9.5%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 29 (9.2%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 8 (10%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 PAD}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 12 (3.1%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 8 (2.5%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 4 (5.2%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 Previous MI}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 33 (8.4%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 24 (7.6%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 9 (12%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 Previous TIA}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 386}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 10 (2.6%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 8 (2.6%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 2 (2.7%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 Pre-stroke mRS  [-1]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 }}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 }}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 }}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 1}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 }}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 348 (89%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 284 (90%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 64 (83%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 2}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 }}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 32 (8.2%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 25 (8.0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 7 (9.1%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 3}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 }}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 10 (2.6%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 5 (1.6%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 5 (6.5%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 4}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 }}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 1 (0.3%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 1 (1.3%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 Acute NIHSS score}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 388}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 3.0 (2.0;5.0) [0.0,32.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 3.0 (2.0;5.0) [0.0,32.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 3.0 (2.0;7.0) [0.0,22.0]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 Any reperfusion therapy}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 151 (39%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 123 (39%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 28 (36%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 Active trial treatment}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 190 (49%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 147 (47%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 43 (56%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 Six month PASE score}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 159 (101;225) [0,486]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 180 (135;240) [86,486]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 57 (34;68) [0,83]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 One month mRS [-1]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 390}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 157 (40%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 136 (43%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 21 (27%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 One month MFI (General fatigue)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 378}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 10.0 (7.0;13.0) [4.0,20.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 10.0 (7.0;13.0) [4.0,20.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 12.0 (8.0;15.2) [4.0,20.0]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 One month MFI (Physical fatigue)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 376}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 10.0 (7.0;14.0) [4.0,20.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 9.0 (7.0;13.0) [4.0,20.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 13.0 (7.8;17.0) [4.0,20.0]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 One month MFI (Reduced activity)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 377}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 10.0 (7.0;13.0) [4.0,20.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 9.0 (6.0;12.0) [4.0,20.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 12.0 (9.0;16.0) [4.0,20.0]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 One month MFI (Reduced motivation)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 378}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 7.00 (5.00;9.00) [4.00,20.00]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 7.00 (5.00;9.00) [4.00,16.00]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 8.00 (5.00;12.00) [4.00,20.00]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 One month MFI (Mental fatigue)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 373}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 7.0 (4.0;11.0) [4.0,20.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 7.0 (4.0;10.0) [4.0,20.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 8.0 (5.0;12.0) [4.0,20.0]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 One month MDI}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 381}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 5.0 (3.0;9.0) [0.0,45.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 5.0 (2.0;8.0) [0.0,37.0]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 8.0 (4.0;15.0) [0.0,45.0]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 One month WHO5}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 385}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 76 (64;88) [0,100]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 76 (64;88) [0,100]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 70 (48;88) [0,100]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 PASE absolute decline}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 19 (-43;69) [-272,407]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 1 (-55;38) [-272,349]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 77 (45;127) [7,407]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 PASE relative decline}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 10 (-25;38) [-221,100]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 1 (-34;22) [-221,68]}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 60 (41;75) [8,100]}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 PASE score difference, relative F}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 PASE score difference, absolute F}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 PASE 0 quartiles}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 130 (33%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 83 (26%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 47 (61%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 PASE 6 quartiles}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 82 (21%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 82 (26%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 PASE first quartile drop F}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1872
+\intbl {\f0 {\f0\fs20 PASE first quartile hop F}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3744
+\intbl {\f0 {\f0\fs20 391}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 0 (0%)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 {\super \i 1}Median (25%;75%) [Minimum,Maximum]; n (%)}}\cell
+
+\row
+
+}

1 PA Decline/archive/generation_1/table2.RTF

@@ -0,0 +1,491 @@
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+
+\paperw12240\paperh15840\widowctrl\ftnbj\fet0\sectd\linex0
+\lndscpsxn
+\margl1440\margr1440\margt1440\margb1440
+\headery720\footery720\fs20
+
+\trowd\trrh0\trhdr
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0\cf1 {\f0\fs20 Model coefficients} {\f0\fs20\i\super } \line {\f0\fs20 Combined table of both full and regularised model coefficients} {\f0\fs20\i\super }}\cell
+
+\row
+
+\trowd\trrh0\trhdr
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 }}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2\clmgf \cellx2340
+\intbl {\f0 {\f0\fs20 Regularised model, (a=1, l=0.031)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2\clmrg \cellx3510
+\intbl {\f0 {\f0\fs20 Regularised model, (a=1, l=0.031)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2\clmrg \cellx4680
+\intbl {\f0 {\f0\fs20 Regularised model, (a=1, l=0.031)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2\clmrg \cellx5850
+\intbl {\f0 {\f0\fs20 Regularised model, (a=1, l=0.031)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2\clmgf \cellx7020
+\intbl {\f0 {\f0\fs20 Full model}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2\clmrg \cellx8190
+\intbl {\f0 {\f0\fs20 Full model}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2\clmrg \cellx9360
+\intbl {\f0 {\f0\fs20 Full model}}\cell
+
+\row
+
+\trowd\trrh0\trhdr
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2 \cellx1170
+\intbl {\f0 {\f0\fs20 name}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2 \cellx2340
+\intbl {\f0 {\f0\fs20 medianX}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2 \cellx3510
+\intbl {\f0 {\f0\fs20 ORmed}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2 \cellx4680
+\intbl {\f0 {\f0\fs20 meanX}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2 \cellx5850
+\intbl {\f0 {\f0\fs20 ORmea}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2 \cellx7020
+\intbl {\f0 {\f0\fs20 coefs}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2 \cellx8190
+\intbl {\f0 {\f0\fs20 OR}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2 \cellx9360
+\intbl {\f0 {\f0\fs20 CIs}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Intercept}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 -3.732}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 0.024}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 -3.773}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 0.023}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 -1.023}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 0.360}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.174,0.757)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Age}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.025}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.026}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.027}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.027}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.484}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.622}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (1.259,2.124)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Male}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 -0.023}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 0.977}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 -0.253}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 0.777}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.445,1.378)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Living alone}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.884}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 2.422}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.888}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 2.431}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 1.170}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 3.224}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (1.874,5.693)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Daily or occasinally smoking}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.156}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.169}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.161}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.175}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.546}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.727}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (1.022,3.04)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 More alcohol than recommendation}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.004}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.004}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.300}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.350}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.583,3.27)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 AFIB}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 -0.060}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 0.942}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.472,1.889)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Hypertension}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.083}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.086}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.100}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.105}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.361}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.434}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.871,2.382)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Diabetes}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.015}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.015}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.437,2.396)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 PAD}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.005}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.005}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.514}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.673}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.4,9.834)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Previous MI}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.047}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.049}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.062}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.064}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.516}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.676}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.727,4.083)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Pre-stroke mRS  [-1]}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.020}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.020}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.271}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.311}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.608,2.904)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Acute NIHSS score}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.033}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.034}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.037}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.037}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.315}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.370}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (1.066,1.788)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Any reperfusion therapy}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 -0.020}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 0.980}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.571,1.706)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Active trial treatment}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.062}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.064}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.106}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.111}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.380}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.462}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.897,2.445)}}\cell
+
+\row
+
+}

1 PA Decline/archive/generation_1/table2_sec.RTF

@@ -0,0 +1,715 @@
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+
+\paperw12240\paperh15840\widowctrl\ftnbj\fet0\sectd\linex0
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+\margl1440\margr1440\margt1440\margb1440
+\headery720\footery720\fs20
+
+\trowd\trrh0\trhdr
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0\cf1 {\f0\fs20 Model coefficients} {\f0\fs20\i\super } \line {\f0\fs20 Combined table of both full and regularised model coefficients} {\f0\fs20\i\super }}\cell
+
+\row
+
+\trowd\trrh0\trhdr
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 }}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2\clmgf \cellx2340
+\intbl {\f0 {\f0\fs20 Regularised model, (a=0.9, l=0.062)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2\clmrg \cellx3510
+\intbl {\f0 {\f0\fs20 Regularised model, (a=0.9, l=0.062)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2\clmrg \cellx4680
+\intbl {\f0 {\f0\fs20 Regularised model, (a=0.9, l=0.062)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2\clmrg \cellx5850
+\intbl {\f0 {\f0\fs20 Regularised model, (a=0.9, l=0.062)}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2\clmgf \cellx7020
+\intbl {\f0 {\f0\fs20 Full model}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2\clmrg \cellx8190
+\intbl {\f0 {\f0\fs20 Full model}}\cell
+
+\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2\clmrg \cellx9360
+\intbl {\f0 {\f0\fs20 Full model}}\cell
+
+\row
+
+\trowd\trrh0\trhdr
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2 \cellx1170
+\intbl {\f0 {\f0\fs20 name}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2 \cellx2340
+\intbl {\f0 {\f0\fs20 medianX}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2 \cellx3510
+\intbl {\f0 {\f0\fs20 ORmed}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2 \cellx4680
+\intbl {\f0 {\f0\fs20 meanX}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2 \cellx5850
+\intbl {\f0 {\f0\fs20 ORmea}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2 \cellx7020
+\intbl {\f0 {\f0\fs20 coefs}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2 \cellx8190
+\intbl {\f0 {\f0\fs20 OR}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85\clbrdrb\brdrs\brdrw20\brdrcf2 \cellx9360
+\intbl {\f0 {\f0\fs20 CIs}}\cell
+
+\row
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+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Intercept}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 -0.890}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 0.411}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 -0.949}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 0.387}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 -1.203}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 0.300}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.112,0.808)}}\cell
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+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Age}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.004}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.004}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.004}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.004}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.373}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.452}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (1.147,1.857)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Male}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.014}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.014}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.597,1.745)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Living alone}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.186}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.204}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.208}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.231}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.702}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 2.017}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (1.23,3.377)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Daily or occasinally smoking}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.003}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.003}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.403}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.496}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.927,2.479)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 More alcohol than recommendation}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 -0.154}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 0.857}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.389,1.898)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 AFIB}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 -0.165}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 0.848}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.451,1.612)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Hypertension}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.241}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.273}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.8,2.07)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Diabetes}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 -0.193}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 0.824}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.385,1.788)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 PAD}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 -0.169}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 0.845}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.245,3.202)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Previous MI}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.591}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.807}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.802,4.422)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Pre-stroke mRS  [-1]}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 -0.028}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 0.973}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 -0.843}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 0.430}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.219,0.892)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Acute NIHSS score}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.001}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.001}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.323}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.382}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (1.084,1.787)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Any reperfusion therapy}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 -0.177}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 0.838}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.496,1.423)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 Active trial treatment}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.007}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.007}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.343}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.409}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.897,2.25)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 One month mRS [-1]}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.077}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.080}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.572}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.773}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.876,4.094)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 One month MFI (General fatigue)}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 -0.565}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 0.568}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.396,0.808)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 One month MFI (Physical fatigue)}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.214}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.238}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.894,1.729)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 One month MFI (Reduced activity)}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.002}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.002}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 0.072}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 1.074}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.765,1.516)}}\cell
+
+\row
+
+\trowd\trrh0
+
+\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170
+\intbl {\f0 {\f0\fs20 One month MFI (Reduced motivation)}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
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+\intbl {\f0 {\f0\fs20 (1.193,2.299)}}\cell
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+\intbl {\f0 {\f0\fs20 One month WHO5}}\cell
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+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340
+\intbl {\f0 {\f0\fs20 0.000}}\cell
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+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510
+\intbl {\f0 {\f0\fs20 1.000}}\cell
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+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680
+\intbl {\f0 {\f0\fs20 0.000}}\cell
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+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850
+\intbl {\f0 {\f0\fs20 1.000}}\cell
+
+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020
+\intbl {\f0 {\f0\fs20 -0.108}}\cell
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+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190
+\intbl {\f0 {\f0\fs20 0.898}}\cell
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+\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360
+\intbl {\f0 {\f0\fs20 (0.653,1.239)}}\cell
+
+\row
+
+}

1 PA Decline/archive/generation_1/table3.RTF

@@ -0,0 +1,184 @@
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+\intbl {\f0\cf1 {\f0\fs20 Performance meassures} {\f0\fs20\i\super } \line {\f0\fs20 Combined table of both full and regularised performance meassures} {\f0\fs20\i\super }}\cell
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+\intbl {\f0 {\f0\fs20 Meassure}}\cell
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+\intbl {\f0 {\f0\fs20 0.609}}\cell
+
+\row
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+}

1 PA Decline/archive/generation_1/table3_sec.RTF

@@ -0,0 +1,184 @@
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+\intbl {\f0\cf1 {\f0\fs20 Performance meassures} {\f0\fs20\i\super } \line {\f0\fs20 Combined table of both full and regularised performance meassures} {\f0\fs20\i\super }}\cell
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+\intbl {\f0 {\f0\fs20 Meassure}}\cell
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1 PA Decline/archive/prediction exercise.R

@@ -0,0 +1,142 @@
+# https://www.machinelearningplus.com/machine-learning/caret-package/
+
+# install.packages(c('caret', 'skimr', 'RANN', 'randomForest', 'fastAdaboost', 'gbm', 'xgboost', 'caretEnsemble', 'C50', 'earth'))
+
+# Load the caret package
+library(caret)
+
+# Import dataset
+orange <- read.csv('https://raw.githubusercontent.com/selva86/datasets/master/orange_juice_withmissing.csv')
+
+# Structure of the dataframe
+str(orange)
+
+# See top 6 rows and 10 columns
+head(orange[, 1:10])
+
+# Create the training and test datasets
+set.seed(100)
+
+# Step 1: Get row numbers for the training data
+trainRowNumbers <- createDataPartition(orange$Purchase, p=0.8, list=FALSE)
+
+# Step 2: Create the training  dataset
+trainData <- orange[trainRowNumbers,]
+
+# Step 3: Create the test dataset
+testData <- orange[-trainRowNumbers,]
+
+# Store X and Y for later use.
+x = trainData[, 2:18]
+y = trainData$Purchase
+
+library(skimr)
+skimmed <- skim(trainData)
+skimmed
+
+# Create the knn imputation model on the training data
+preProcess_missingdata_model <- preProcess(trainData, method='knnImpute')
+preProcess_missingdata_model
+
+# Use the imputation model to predict the values of missing data points
+library(RANN)  # required for knnInpute
+trainData <- predict(preProcess_missingdata_model, newdata = trainData)
+anyNA(trainData)
+
+# One-Hot Encoding
+# Creating dummy variables is converting a categorical variable to as many binary variables as here are categories.
+dummies_model <- dummyVars(Purchase ~ ., data=trainData)
+
+# Create the dummy variables using predict. The Y variable (Purchase) will not be present in trainData_mat.
+trainData_mat <- predict(dummies_model, newdata = trainData)
+
+# # Convert to dataframe
+trainData <- data.frame(trainData_mat)
+
+# # See the structure of the new dataset
+str(trainData)
+
+
+preProcess_range_model <- preProcess(trainData, method='range')
+trainData <- predict(preProcess_range_model, newdata = trainData)
+
+# Append the Y variable
+trainData$Purchase <- y
+
+apply(trainData[, 1:10], 2, FUN=function(x){c('min'=min(x), 'max'=max(x))})
+
+
+featurePlot(x=trainData[,1:18],
+            y=factor(trainData$Purchase),
+            plot="box",
+            strip=strip.custom(par.strip.text=list(cex=.7)),
+            scales = list(x = list(relation="free"),
+                          y = list(relation="free")))
+
+featurePlot(x=trainData[,1:18],
+            y=factor(trainData$Purchase),
+            plot="density",
+            strip=strip.custom(par.strip.text=list(cex=.7)),
+            scales = list(x = list(relation="free"),
+                          y = list(relation="free")))
+
+# 5
+
+set.seed(100)
+options(warn=-1)
+
+subsets <- c(1:5, 10, 15, 18)
+
+ctrl <- rfeControl(functions = rfFuncs,
+                   method = "repeatedcv",
+                   repeats = 5,
+                   verbose = FALSE)
+
+lmProfile <- rfe(x=trainData[, 1:18], y=factor(trainData$Purchase),
+                 sizes = subsets,
+                 rfeControl = ctrl)
+
+lmProfile
+
+
+# See available algorithms in caret
+modelnames <- dput(names(getModelInfo()))
+# modelnames <- paste(names(getModelInfo()), collapse=',  ')
+modelnames
+
+
+# Set the seed for reproducibility
+set.seed(100)
+
+# Train the model using randomForest and predict on the training data itself.
+model_mars = train(Purchase ~ ., data=trainData, method='earth')
+fitted <- predict(model_mars)
+
+model_mars
+
+
+plot(model_mars, main="Model Accuracies with MARS")
+
+varimp_mars <- varImp(model_mars)
+plot(varimp_mars, main="Variable Importance with MARS")
+
+
+## 6.4
+
+# Step 1: Impute missing values 
+testData2 <- predict(preProcess_missingdata_model, testData)  
+
+# Step 2: Create one-hot encodings (dummy variables)
+testData3 <- predict(dummies_model, testData2)
+
+# Step 3: Transform the features to range between 0 and 1
+testData4 <- predict(preProcess_range_model, testData3)
+
+# View
+head(testData4[, 1:10])
+
+predicted <- predict(model_mars, testData4)
+head(predicted)
+
+# Compute the confusion matrix
+confusionMatrix(reference = factor(testData$Purchase), data = predicted, mode='everything', positive='MM')

1 PA Decline/archive/predictive_model.Rmd

@@ -0,0 +1,101 @@
+---
+title: "predictive_model"
+output: pdf_document
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+# Data
+```{r}
+library(caret)
+library(pROC)
+library(daDoctoR)
+library(dplyr)
+```
+
+Import
+```{r}
+trainData<-read.csv("/Users/au301842/PhysicalActivityandStrokeOutcome/data/trainData.csv",)
+testData<-read.csv("/Users/au301842/PhysicalActivityandStrokeOutcome/data/testData.csv",)
+```
+
+
+# Prediction
+Inspiration: https://stackoverflow.com/questions/30366143/how-to-compute-roc-and-auc-under-roc-after-training-using-caret-in-r and https://www.machinelearningplus.com/machine-learning/caret-package/
+
+## Early visualisation
+
+```{r}
+featurePlot(x = trainData %>% select(!matches("pase_drop")),
+            y = factor(trainData$pase_drop), 
+            plot = "box",
+            strip=strip.custom(par.strip.text=list(cex=.7)),
+            scales = list(x = list(relation="free"), 
+                          y = list(relation="free")))
+
+featurePlot(x = trainData %>% select(!matches("pase_drop")),
+            y = factor(trainData$pase_drop), 
+            plot = "density",
+            strip=strip.custom(par.strip.text=list(cex=.7)),
+            scales = list(x = list(relation="free"), 
+                          y = list(relation="free")))
+```
+
+
+```{r}
+subsets <- c(1:10, 15, 18,33)
+
+ctrl <- rfeControl(functions = rfFuncs,
+                   method = "repeatedcv",
+                   repeats = 5,
+                   verbose = FALSE)
+
+lmProfile <- rfe(x = trainData %>% select(!matches("pase_drop")), 
+                 y = trainData$pase_drop,
+                 sizes = subsets,
+                 rfeControl = ctrl)
+
+lmProfile
+```
+
+
+```{r}
+set.seed(1000)
+
+forest.model <- train(pase_drop ~., trainData)
+
+result.predicted.prob <- predict(forest.model, testData, type="prob") # Prediction
+
+result.roc <- roc(factor(testData$pase_drop), result.predicted.prob$no) # Draw ROC curve. 
+
+plot(result.roc, print.thres="best", print.thres.best.method="closest.topleft")
+
+result.coords <- coords(result.roc, "best", best.method="closest.topleft", ret=c("threshold", "accuracy"))
+print(result.coords)#to get threshold and accuracy
+```
+
+```{r}
+library(MLeval)
+
+myTrainingControl <- trainControl(method = "cv", 
+                                  number = 10, 
+                                  savePredictions = TRUE, 
+                                  classProbs = TRUE, 
+                                  verboseIter = TRUE)
+
+randomForestFit = train(x = trainData[,1:32], 
+                        y = as.factor(trainData$pase_drop), 
+                        method = "rf", 
+                        trControl = myTrainingControl, 
+                        preProcess = c("center","scale"), 
+                        ntree = 50)
+
+x <- evalm(randomForestFit)
+
+x$roc
+
+x$stdres
+```
+

1 PA Decline/archive/regularised model.R

@@ -0,0 +1,6 @@
+## 
+## Regularisation
+## 
+## 
+
+

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1 PA Decline/article ready_files/libs/quarto-html/quarto.js

@@ -0,0 +1,899 @@
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+      sectionIndex = [...sections].reverse().findIndex((section) => {
+        if (section) {
+          return window.pageYOffset >= section.offsetTop - sectionMargin;
+        } else {
+          return false;
+        }
+      });
+    }
+    if (sectionIndex > -1) {
+      const current = sections.length - sectionIndex - 1;
+      if (current !== currentActive) {
+        removeAllActive();
+        currentActive = current;
+        makeActive(current);
+        if (init) {
+          window.dispatchEvent(sectionChanged);
+        }
+        init = true;
+      }
+    }
+  };
+
+  const inHiddenRegion = (top, bottom, hiddenRegions) => {
+    for (const region of hiddenRegions) {
+      if (top <= region.bottom && bottom >= region.top) {
+        return true;
+      }
+    }
+    return false;
+  };
+
+  const categorySelector = "header.quarto-title-block .quarto-category";
+  const activateCategories = (href) => {
+    // Find any categories
+    // Surround them with a link pointing back to:
+    // #category=Authoring
+    try {
+      const categoryEls = window.document.querySelectorAll(categorySelector);
+      for (const categoryEl of categoryEls) {
+        const categoryText = categoryEl.textContent;
+        if (categoryText) {
+          const link = `${href}#category=${encodeURIComponent(categoryText)}`;
+          const linkEl = window.document.createElement("a");
+          linkEl.setAttribute("href", link);
+          for (const child of categoryEl.childNodes) {
+            linkEl.append(child);
+          }
+          categoryEl.appendChild(linkEl);
+        }
+      }
+    } catch {
+      // Ignore errors
+    }
+  };
+  function hasTitleCategories() {
+    return window.document.querySelector(categorySelector) !== null;
+  }
+
+  function offsetRelativeUrl(url) {
+    const offset = getMeta("quarto:offset");
+    return offset ? offset + url : url;
+  }
+
+  function offsetAbsoluteUrl(url) {
+    const offset = getMeta("quarto:offset");
+    const baseUrl = new URL(offset, window.location);
+
+    const projRelativeUrl = url.replace(baseUrl, "");
+    if (projRelativeUrl.startsWith("/")) {
+      return projRelativeUrl;
+    } else {
+      return "/" + projRelativeUrl;
+    }
+  }
+
+  // read a meta tag value
+  function getMeta(metaName) {
+    const metas = window.document.getElementsByTagName("meta");
+    for (let i = 0; i < metas.length; i++) {
+      if (metas[i].getAttribute("name") === metaName) {
+        return metas[i].getAttribute("content");
+      }
+    }
+    return "";
+  }
+
+  async function findAndActivateCategories() {
+    const currentPagePath = offsetAbsoluteUrl(window.location.href);
+    const response = await fetch(offsetRelativeUrl("listings.json"));
+    if (response.status == 200) {
+      return response.json().then(function (listingPaths) {
+        const listingHrefs = [];
+        for (const listingPath of listingPaths) {
+          const pathWithoutLeadingSlash = listingPath.listing.substring(1);
+          for (const item of listingPath.items) {
+            if (
+              item === currentPagePath ||
+              item === currentPagePath + "index.html"
+            ) {
+              // Resolve this path against the offset to be sure
+              // we already are using the correct path to the listing
+              // (this adjusts the listing urls to be rooted against
+              // whatever root the page is actually running against)
+              const relative = offsetRelativeUrl(pathWithoutLeadingSlash);
+              const baseUrl = window.location;
+              const resolvedPath = new URL(relative, baseUrl);
+              listingHrefs.push(resolvedPath.pathname);
+              break;
+            }
+          }
+        }
+
+        // Look up the tree for a nearby linting and use that if we find one
+        const nearestListing = findNearestParentListing(
+          offsetAbsoluteUrl(window.location.pathname),
+          listingHrefs
+        );
+        if (nearestListing) {
+          activateCategories(nearestListing);
+        } else {
+          // See if the referrer is a listing page for this item
+          const referredRelativePath = offsetAbsoluteUrl(document.referrer);
+          const referrerListing = listingHrefs.find((listingHref) => {
+            const isListingReferrer =
+              listingHref === referredRelativePath ||
+              listingHref === referredRelativePath + "index.html";
+            return isListingReferrer;
+          });
+
+          if (referrerListing) {
+            // Try to use the referrer if possible
+            activateCategories(referrerListing);
+          } else if (listingHrefs.length > 0) {
+            // Otherwise, just fall back to the first listing
+            activateCategories(listingHrefs[0]);
+          }
+        }
+      });
+    }
+  }
+  if (hasTitleCategories()) {
+    findAndActivateCategories();
+  }
+
+  const findNearestParentListing = (href, listingHrefs) => {
+    if (!href || !listingHrefs) {
+      return undefined;
+    }
+    // Look up the tree for a nearby linting and use that if we find one
+    const relativeParts = href.substring(1).split("/");
+    while (relativeParts.length > 0) {
+      const path = relativeParts.join("/");
+      for (const listingHref of listingHrefs) {
+        if (listingHref.startsWith(path)) {
+          return listingHref;
+        }
+      }
+      relativeParts.pop();
+    }
+
+    return undefined;
+  };
+
+  const manageSidebarVisiblity = (el, placeholderDescriptor) => {
+    let isVisible = true;
+    let elRect;
+
+    return (hiddenRegions) => {
+      if (el === null) {
+        return;
+      }
+
+      // Find the last element of the TOC
+      const lastChildEl = el.lastElementChild;
+
+      if (lastChildEl) {
+        // Converts the sidebar to a menu
+        const convertToMenu = () => {
+          for (const child of el.children) {
+            child.style.opacity = 0;
+            child.style.overflow = "hidden";
+          }
+
+          nexttick(() => {
+            const toggleContainer = window.document.createElement("div");
+            toggleContainer.style.width = "100%";
+            toggleContainer.classList.add("zindex-over-content");
+            toggleContainer.classList.add("quarto-sidebar-toggle");
+            toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom
+            toggleContainer.id = placeholderDescriptor.id;
+            toggleContainer.style.position = "fixed";
+
+            const toggleIcon = window.document.createElement("i");
+            toggleIcon.classList.add("quarto-sidebar-toggle-icon");
+            toggleIcon.classList.add("bi");
+            toggleIcon.classList.add("bi-caret-down-fill");
+
+            const toggleTitle = window.document.createElement("div");
+            const titleEl = window.document.body.querySelector(
+              placeholderDescriptor.titleSelector
+            );
+            if (titleEl) {
+              toggleTitle.append(
+                titleEl.textContent || titleEl.innerText,
+                toggleIcon
+              );
+            }
+            toggleTitle.classList.add("zindex-over-content");
+            toggleTitle.classList.add("quarto-sidebar-toggle-title");
+            toggleContainer.append(toggleTitle);
+
+            const toggleContents = window.document.createElement("div");
+            toggleContents.classList = el.classList;
+            toggleContents.classList.add("zindex-over-content");
+            toggleContents.classList.add("quarto-sidebar-toggle-contents");
+            for (const child of el.children) {
+              if (child.id === "toc-title") {
+                continue;
+              }
+
+              const clone = child.cloneNode(true);
+              clone.style.opacity = 1;
+              clone.style.display = null;
+              toggleContents.append(clone);
+            }
+            toggleContents.style.height = "0px";
+            const positionToggle = () => {
+              // position the element (top left of parent, same width as parent)
+              if (!elRect) {
+                elRect = el.getBoundingClientRect();
+              }
+              toggleContainer.style.left = `${elRect.left}px`;
+              toggleContainer.style.top = `${elRect.top}px`;
+              toggleContainer.style.width = `${elRect.width}px`;
+            };
+            positionToggle();
+
+            toggleContainer.append(toggleContents);
+            el.parentElement.prepend(toggleContainer);
+
+            // Process clicks
+            let tocShowing = false;
+            // Allow the caller to control whether this is dismissed
+            // when it is clicked (e.g. sidebar navigation supports
+            // opening and closing the nav tree, so don't dismiss on click)
+            const clickEl = placeholderDescriptor.dismissOnClick
+              ? toggleContainer
+              : toggleTitle;
+
+            const closeToggle = () => {
+              if (tocShowing) {
+                toggleContainer.classList.remove("expanded");
+                toggleContents.style.height = "0px";
+                tocShowing = false;
+              }
+            };
+
+            // Get rid of any expanded toggle if the user scrolls
+            window.document.addEventListener(
+              "scroll",
+              throttle(() => {
+                closeToggle();
+              }, 50)
+            );
+
+            // Handle positioning of the toggle
+            window.addEventListener(
+              "resize",
+              throttle(() => {
+                elRect = undefined;
+                positionToggle();
+              }, 50)
+            );
+
+            window.addEventListener("quarto-hrChanged", () => {
+              elRect = undefined;
+            });
+
+            // Process the click
+            clickEl.onclick = () => {
+              if (!tocShowing) {
+                toggleContainer.classList.add("expanded");
+                toggleContents.style.height = null;
+                tocShowing = true;
+              } else {
+                closeToggle();
+              }
+            };
+          });
+        };
+
+        // Converts a sidebar from a menu back to a sidebar
+        const convertToSidebar = () => {
+          for (const child of el.children) {
+            child.style.opacity = 1;
+            child.style.overflow = null;
+          }
+
+          const placeholderEl = window.document.getElementById(
+            placeholderDescriptor.id
+          );
+          if (placeholderEl) {
+            placeholderEl.remove();
+          }
+
+          el.classList.remove("rollup");
+        };
+
+        if (isReaderMode()) {
+          convertToMenu();
+          isVisible = false;
+        } else {
+          // Find the top and bottom o the element that is being managed
+          const elTop = el.offsetTop;
+          const elBottom =
+            elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;
+
+          if (!isVisible) {
+            // If the element is current not visible reveal if there are
+            // no conflicts with overlay regions
+            if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {
+              convertToSidebar();
+              isVisible = true;
+            }
+          } else {
+            // If the element is visible, hide it if it conflicts with overlay regions
+            // and insert a placeholder toggle (or if we're in reader mode)
+            if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {
+              convertToMenu();
+              isVisible = false;
+            }
+          }
+        }
+      }
+    };
+  };
+
+  const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]');
+  for (const tabEl of tabEls) {
+    const id = tabEl.getAttribute("data-bs-target");
+    if (id) {
+      const columnEl = document.querySelector(
+        `${id} .column-margin, .tabset-margin-content`
+      );
+      if (columnEl)
+        tabEl.addEventListener("shown.bs.tab", function (event) {
+          const el = event.srcElement;
+          if (el) {
+            const visibleCls = `${el.id}-margin-content`;
+            // walk up until we find a parent tabset
+            let panelTabsetEl = el.parentElement;
+            while (panelTabsetEl) {
+              if (panelTabsetEl.classList.contains("panel-tabset")) {
+                break;
+              }
+              panelTabsetEl = panelTabsetEl.parentElement;
+            }
+
+            if (panelTabsetEl) {
+              const prevSib = panelTabsetEl.previousElementSibling;
+              if (
+                prevSib &&
+                prevSib.classList.contains("tabset-margin-container")
+              ) {
+                const childNodes = prevSib.querySelectorAll(
+                  ".tabset-margin-content"
+                );
+                for (const childEl of childNodes) {
+                  if (childEl.classList.contains(visibleCls)) {
+                    childEl.classList.remove("collapse");
+                  } else {
+                    childEl.classList.add("collapse");
+                  }
+                }
+              }
+            }
+          }
+
+          layoutMarginEls();
+        });
+    }
+  }
+
+  // Manage the visibility of the toc and the sidebar
+  const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
+    id: "quarto-toc-toggle",
+    titleSelector: "#toc-title",
+    dismissOnClick: true,
+  });
+  const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
+    id: "quarto-sidebarnav-toggle",
+    titleSelector: ".title",
+    dismissOnClick: false,
+  });
+  let tocLeftScrollVisibility;
+  if (leftTocEl) {
+    tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
+      id: "quarto-lefttoc-toggle",
+      titleSelector: "#toc-title",
+      dismissOnClick: true,
+    });
+  }
+
+  // Find the first element that uses formatting in special columns
+  const conflictingEls = window.document.body.querySelectorAll(
+    '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
+  );
+
+  // Filter all the possibly conflicting elements into ones
+  // the do conflict on the left or ride side
+  const arrConflictingEls = Array.from(conflictingEls);
+  const leftSideConflictEls = arrConflictingEls.filter((el) => {
+    if (el.tagName === "ASIDE") {
+      return false;
+    }
+    return Array.from(el.classList).find((className) => {
+      return (
+        className !== "column-body" &&
+        className.startsWith("column-") &&
+        !className.endsWith("right") &&
+        !className.endsWith("container") &&
+        className !== "column-margin"
+      );
+    });
+  });
+  const rightSideConflictEls = arrConflictingEls.filter((el) => {
+    if (el.tagName === "ASIDE") {
+      return true;
+    }
+
+    const hasMarginCaption = Array.from(el.classList).find((className) => {
+      return className == "margin-caption";
+    });
+    if (hasMarginCaption) {
+      return true;
+    }
+
+    return Array.from(el.classList).find((className) => {
+      return (
+        className !== "column-body" &&
+        !className.endsWith("container") &&
+        className.startsWith("column-") &&
+        !className.endsWith("left")
+      );
+    });
+  });
+
+  const kOverlapPaddingSize = 10;
+  function toRegions(els) {
+    return els.map((el) => {
+      const boundRect = el.getBoundingClientRect();
+      const top =
+        boundRect.top +
+        document.documentElement.scrollTop -
+        kOverlapPaddingSize;
+      return {
+        top,
+        bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
+      };
+    });
+  }
+
+  let hasObserved = false;
+  const visibleItemObserver = (els) => {
+    let visibleElements = [...els];
+    const intersectionObserver = new IntersectionObserver(
+      (entries, _observer) => {
+        entries.forEach((entry) => {
+          if (entry.isIntersecting) {
+            if (visibleElements.indexOf(entry.target) === -1) {
+              visibleElements.push(entry.target);
+            }
+          } else {
+            visibleElements = visibleElements.filter((visibleEntry) => {
+              return visibleEntry !== entry;
+            });
+          }
+        });
+
+        if (!hasObserved) {
+          hideOverlappedSidebars();
+        }
+        hasObserved = true;
+      },
+      {}
+    );
+    els.forEach((el) => {
+      intersectionObserver.observe(el);
+    });
+
+    return {
+      getVisibleEntries: () => {
+        return visibleElements;
+      },
+    };
+  };
+
+  const rightElementObserver = visibleItemObserver(rightSideConflictEls);
+  const leftElementObserver = visibleItemObserver(leftSideConflictEls);
+
+  const hideOverlappedSidebars = () => {
+    marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries()));
+    sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries()));
+    if (tocLeftScrollVisibility) {
+      tocLeftScrollVisibility(
+        toRegions(leftElementObserver.getVisibleEntries())
+      );
+    }
+  };
+
+  window.quartoToggleReader = () => {
+    // Applies a slow class (or removes it)
+    // to update the transition speed
+    const slowTransition = (slow) => {
+      const manageTransition = (id, slow) => {
+        const el = document.getElementById(id);
+        if (el) {
+          if (slow) {
+            el.classList.add("slow");
+          } else {
+            el.classList.remove("slow");
+          }
+        }
+      };
+
+      manageTransition("TOC", slow);
+      manageTransition("quarto-sidebar", slow);
+    };
+    const readerMode = !isReaderMode();
+    setReaderModeValue(readerMode);
+
+    // If we're entering reader mode, slow the transition
+    if (readerMode) {
+      slowTransition(readerMode);
+    }
+    highlightReaderToggle(readerMode);
+    hideOverlappedSidebars();
+
+    // If we're exiting reader mode, restore the non-slow transition
+    if (!readerMode) {
+      slowTransition(!readerMode);
+    }
+  };
+
+  const highlightReaderToggle = (readerMode) => {
+    const els = document.querySelectorAll(".quarto-reader-toggle");
+    if (els) {
+      els.forEach((el) => {
+        if (readerMode) {
+          el.classList.add("reader");
+        } else {
+          el.classList.remove("reader");
+        }
+      });
+    }
+  };
+
+  const setReaderModeValue = (val) => {
+    if (window.location.protocol !== "file:") {
+      window.localStorage.setItem("quarto-reader-mode", val);
+    } else {
+      localReaderMode = val;
+    }
+  };
+
+  const isReaderMode = () => {
+    if (window.location.protocol !== "file:") {
+      return window.localStorage.getItem("quarto-reader-mode") === "true";
+    } else {
+      return localReaderMode;
+    }
+  };
+  let localReaderMode = null;
+
+  const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded");
+  const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1;
+
+  // Walk the TOC and collapse/expand nodes
+  // Nodes are expanded if:
+  // - they are top level
+  // - they have children that are 'active' links
+  // - they are directly below an link that is 'active'
+  const walk = (el, depth) => {
+    // Tick depth when we enter a UL
+    if (el.tagName === "UL") {
+      depth = depth + 1;
+    }
+
+    // It this is active link
+    let isActiveNode = false;
+    if (el.tagName === "A" && el.classList.contains("active")) {
+      isActiveNode = true;
+    }
+
+    // See if there is an active child to this element
+    let hasActiveChild = false;
+    for (child of el.children) {
+      hasActiveChild = walk(child, depth) || hasActiveChild;
+    }
+
+    // Process the collapse state if this is an UL
+    if (el.tagName === "UL") {
+      if (tocOpenDepth === -1 && depth > 1) {
+        el.classList.add("collapse");
+      } else if (
+        depth <= tocOpenDepth ||
+        hasActiveChild ||
+        prevSiblingIsActiveLink(el)
+      ) {
+        el.classList.remove("collapse");
+      } else {
+        el.classList.add("collapse");
+      }
+
+      // untick depth when we leave a UL
+      depth = depth - 1;
+    }
+    return hasActiveChild || isActiveNode;
+  };
+
+  // walk the TOC and expand / collapse any items that should be shown
+
+  if (tocEl) {
+    walk(tocEl, 0);
+    updateActiveLink();
+  }
+
+  // Throttle the scroll event and walk peridiocally
+  window.document.addEventListener(
+    "scroll",
+    throttle(() => {
+      if (tocEl) {
+        updateActiveLink();
+        walk(tocEl, 0);
+      }
+      if (!isReaderMode()) {
+        hideOverlappedSidebars();
+      }
+    }, 5)
+  );
+  window.addEventListener(
+    "resize",
+    throttle(() => {
+      if (!isReaderMode()) {
+        hideOverlappedSidebars();
+      }
+    }, 10)
+  );
+  hideOverlappedSidebars();
+  highlightReaderToggle(isReaderMode());
+});
+
+// grouped tabsets
+window.addEventListener("pageshow", (_event) => {
+  function getTabSettings() {
+    const data = localStorage.getItem("quarto-persistent-tabsets-data");
+    if (!data) {
+      localStorage.setItem("quarto-persistent-tabsets-data", "{}");
+      return {};
+    }
+    if (data) {
+      return JSON.parse(data);
+    }
+  }
+
+  function setTabSettings(data) {
+    localStorage.setItem(
+      "quarto-persistent-tabsets-data",
+      JSON.stringify(data)
+    );
+  }
+
+  function setTabState(groupName, groupValue) {
+    const data = getTabSettings();
+    data[groupName] = groupValue;
+    setTabSettings(data);
+  }
+
+  function toggleTab(tab, active) {
+    const tabPanelId = tab.getAttribute("aria-controls");
+    const tabPanel = document.getElementById(tabPanelId);
+    if (active) {
+      tab.classList.add("active");
+      tabPanel.classList.add("active");
+    } else {
+      tab.classList.remove("active");
+      tabPanel.classList.remove("active");
+    }
+  }
+
+  function toggleAll(selectedGroup, selectorsToSync) {
+    for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
+      const active = selectedGroup === thisGroup;
+      for (const tab of tabs) {
+        toggleTab(tab, active);
+      }
+    }
+  }
+
+  function findSelectorsToSyncByLanguage() {
+    const result = {};
+    const tabs = Array.from(
+      document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
+    );
+    for (const item of tabs) {
+      const div = item.parentElement.parentElement.parentElement;
+      const group = div.getAttribute("data-group");
+      if (!result[group]) {
+        result[group] = {};
+      }
+      const selectorsToSync = result[group];
+      const value = item.innerHTML;
+      if (!selectorsToSync[value]) {
+        selectorsToSync[value] = [];
+      }
+      selectorsToSync[value].push(item);
+    }
+    return result;
+  }
+
+  function setupSelectorSync() {
+    const selectorsToSync = findSelectorsToSyncByLanguage();
+    Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
+      Object.entries(tabSetsByValue).forEach(([value, items]) => {
+        items.forEach((item) => {
+          item.addEventListener("click", (_event) => {
+            setTabState(group, value);
+            toggleAll(value, selectorsToSync[group]);
+          });
+        });
+      });
+    });
+    return selectorsToSync;
+  }
+
+  const selectorsToSync = setupSelectorSync();
+  for (const [group, selectedName] of Object.entries(getTabSettings())) {
+    const selectors = selectorsToSync[group];
+    // it's possible that stale state gives us empty selections, so we explicitly check here.
+    if (selectors) {
+      toggleAll(selectedName, selectors);
+    }
+  }
+});
+
+function throttle(func, wait) {
+  let waiting = false;
+  return function () {
+    if (!waiting) {
+      func.apply(this, arguments);
+      waiting = true;
+      setTimeout(function () {
+        waiting = false;
+      }, wait);
+    }
+  };
+}
+
+function nexttick(func) {
+  return setTimeout(func, 0);
+}

1 PA Decline/article ready_files/libs/quarto-html/tippy.css

@@ -0,0 +1 @@
+.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 10px)}.tippy-box{position:relative;background-color:#333;color:#fff;border-radius:4px;font-size:14px;line-height:1.4;white-space:normal;outline:0;transition-property:transform,visibility,opacity}.tippy-box[data-placement^=top]>.tippy-arrow{bottom:0}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-7px;left:0;border-width:8px 8px 0;border-top-color:initial;transform-origin:center top}.tippy-box[data-placement^=bottom]>.tippy-arrow{top:0}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-7px;left:0;border-width:0 8px 8px;border-bottom-color:initial;transform-origin:center bottom}.tippy-box[data-placement^=left]>.tippy-arrow{right:0}.tippy-box[data-placement^=left]>.tippy-arrow:before{border-width:8px 0 8px 8px;border-left-color:initial;right:-7px;transform-origin:center left}.tippy-box[data-placement^=right]>.tippy-arrow{left:0}.tippy-box[data-placement^=right]>.tippy-arrow:before{left:-7px;border-width:8px 8px 8px 0;border-right-color:initial;transform-origin:center right}.tippy-box[data-inertia][data-state=visible]{transition-timing-function:cubic-bezier(.54,1.5,.38,1.11)}.tippy-arrow{width:16px;height:16px;color:#333}.tippy-arrow:before{content:"";position:absolute;border-color:transparent;border-style:solid}.tippy-content{position:relative;padding:5px 9px;z-index:1}

1 PA Decline/article sankey.R

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+# source("1 PA Decline/data_format.R")
+
+# NEW QUARTILES
+
+ds <- readr::read_csv(here::here("/Volumes/Data/REDCap/DDV/talos_ddv.csv"))
+
+df_raw <- ds |>
+  dplyr::filter(!pase_score_missings_0, !pase_score_missings_4) |>
+  dplyr::transmute(
+    pase_0_cut = as.numeric(stRoke::quantile_cut(x = pase_score_sum_0, 
+                                      groups = 4, 
+                                      group.names = paste0(1:4))),
+    pase_6_cut = as.numeric(stRoke::quantile_cut(x = pase_score_sum_4, 
+                                      y = pase_score_sum_0, 
+                                      groups = 4, 
+                                      inc.outs = TRUE, 
+                                      group.names = paste0(1:4))),
+    change = dplyr::case_when(
+      pase_0_cut %in% 2:4 & pase_6_cut == 1 ~ "drop",
+      pase_6_cut %in% 2:4 & pase_0_cut == 1 ~ "hop",
+      pase_0_cut %in% 2:4 & pase_6_cut %in% 2:4 ~ "hh",
+      pase_0_cut %in% 1 & pase_6_cut == 1 ~ "ll"
+      )
+    ,
+    change_any = factor(dplyr::case_when(
+      pase_6_cut > pase_0_cut ~ "hop",
+      pase_6_cut < pase_0_cut ~ "drop",
+      pase_0_cut %in% 2:4 & pase_6_cut %in% 2:4 ~ "hh",
+      pase_0_cut %in% 1 & pase_6_cut == 1 ~ "ll"
+    )
+    )
+  )
+
+
+# Visuals - sankey
+# https://stackoverflow.com/questions/50395027/beautifying-sankey-alluvial-visualization-using-r
+
+
+## Painting
+
+sankey_ready <- function(data,change.var="change"){
+df <- data |> 
+  dplyr::count(dplyr::across(dplyr::all_of(c("pase_0_cut", "pase_6_cut",change.var)))) |> 
+  dplyr::mutate(dplyr::across(dplyr::starts_with("pase_"),\(.x) factor(.x))) |> 
+  setNames(c("pase_0_cut", "pase_6_cut","change","n"))
+
+lbs0 <-
+  c(
+    paste0("1st \n(n=", sum(df$n[df$pase_0_cut == "1"]), ")"),
+    paste0("2nd \n(n=", sum(df$n[df$pase_0_cut == "2"]), ")"),
+    paste0("3rd \n(n=", sum(df$n[df$pase_0_cut == "3"]), ")"),
+    paste0("4th \n(n=", sum(df$n[df$pase_0_cut == "4"]), ")")
+  )
+
+
+lbs6 <-
+  c(
+    paste0("1st \n(n=", sum(df$n[df$pase_6_cut == "1"]), ")"),
+    paste0("2nd \n(n=", sum(df$n[df$pase_6_cut == "2"]), ")"),
+    paste0("3rd \n(n=", sum(df$n[df$pase_6_cut == "3"]), ")"),
+    paste0("4th \n(n=", sum(df$n[df$pase_6_cut == "4"]), ")")
+  )
+
+
+levels(df$pase_0_cut) <- lbs0[1:length(levels(df$pase_0_cut))]
+levels(df$pase_6_cut) <- lbs6[1:length(levels(df$pase_6_cut))]
+
+df$pase_0_cut <- factor(df$pase_0_cut, levels = rev(levels(df$pase_0_cut)))
+df$pase_6_cut <- factor(df$pase_6_cut, levels = rev(levels(df$pase_6_cut)))
+
+df$change <- factor(df$change, levels = c("hh","hop", "drop", "ll"))
+
+if (change.var=="change"){
+  df |> dplyr::mutate(first_grp=ifelse(substr(pase_0_cut,1,1)==1,"low","higher"))
+} else if (change.var=="change_any"){
+  df |> dplyr::mutate(first_grp=dplyr::case_when(
+    substr(pase_0_cut,1,1)==1 ~ "low",
+    substr(pase_0_cut,1,1) %in% 2:3 ~ "mid",
+    substr(pase_0_cut,1,1)==4 ~ "high"))
+}
+}
+
+# hops <- "#66c1a3" # grey
+# # drops <- "#990033"  # Midtrød
+# drops <- "#CE0045" # Lighter Midtrød
+# nos <- "grey80" # Light grey
+# 
+# # border <- "#00596B"
+# # box <- "#008099"
+# 
+# border <- "#EA571D"
+# box <- "#1E4B66"
+# 
+# higher <- "yellow"
+# low <- "purple"
+
+library(ggalluvial)
+
+library(ggplot2)
+
+# stRoke::color_plot(viridisLite::turbo (4))
+
+plot_sankey <- function(data,
+                        # palette=viridisLite::turbo(4),
+                        hops = "#66c1a3",
+                        drops = "#CE0045",
+                        hh = "#fcdc9c",
+                        ll = "#fcdc9c",
+                        border = "#EA571D",
+                        box = "#1E4B66",
+                        higher = "#2986cc",
+                        mid = "#b4a7d6",
+                        low = "#590075",
+                        alpha = 0.8,
+                        a1=pase_0_cut,
+                        a2=pase_6_cut,
+                        a1.grp=first_grp,
+                        text.size = 4
+                        ){
+  
+  if (length(unique(data[[ncol(data)]]))>2) {
+    fills <- c(higher,low,mid)
+  } else {
+    fills <- c(higher,low) 
+  }
+  
+  cls <- c(hh, hops, drops, ll)
+  # stratum.grp <- c(df[["first_grp"]],df[["last_grp"]])
+  
+  # cls <- palette
+  # browser()
+  ggplot(data, aes(y = n, axis1 = {{a1}}, axis2 = {{a2}})) +
+    geom_alluvium(
+      aes(fill = change, color = change),
+      width = 1 / 16,
+      alpha = alpha,
+      knot.pos = 0.4,
+      curve_type ="sigmoid"
+    ) +
+    geom_stratum(aes(fill={{a1.grp}}),
+                 # geom_stratum(aes(fill=stratum_grp),
+                 size = 2, 
+                 width = 1 / 3.4,
+                 # fill = box,
+                 color = border
+    ) +
+    geom_text(stat = "stratum", 
+              aes(label = after_stat(stratum)), 
+              colour = "white", 
+              size = text.size,
+              lineheight = 1) +
+    scale_x_continuous(
+      breaks = 1:2,
+      labels = c("Pre-stroke\nPASE quartile", "Six months\nPASE quartile")
+    ) +
+    scale_fill_manual(values = c(cls,fills),na.value = box) +
+    scale_color_manual(values = cls) +
+    ggtitle("PA level changes from \npre-stroke to post-stroke")
+}
+
+## Changes to left colum coloring is needed.
+
+c("change","change_any") |> purrr::map(\(.x){
+  df_raw |>
+    sankey_ready(change.var = .x)
+}) |> 
+  purrr::map(\(.x){
+    .x |> plot_sankey(text.size=4.5)
+  }) |> 
+  patchwork::wrap_plots()
+
+
+
+p_delta <- df_raw |>
+  sankey_ready() |> 
+  plot_sankey(text.size=4.5)
+
+
+# plotly::ggplotly(p_delta)
+
+# png(
+#   filename = "sankey_change_ARTICLEA.png",
+#   units = "mm",
+#   width = 500,
+#   height = 600,
+#   pointsize = 60,
+#   res = 300
+# )
+ggplot2::ggsave(filename = "1 PA Decline/sankey_change_ARTICLEA_ejn.png",
+                p_delta +
+                  theme_void() +
+                  theme(
+                    legend.position = "none",
+                    # panel.grid.major = element_blank(),
+                    # panel.grid.minor = element_blank(),
+                    # axis.text.y = element_blank(),
+                    # axis.title.y = element_blank(),
+                    axis.text.x = element_text(),
+                    # text = element_text(size = 5),
+                    plot.title = element_blank(),
+                    # panel.background = element_rect(fill = "white"),
+                    plot.background = element_rect(fill="white"),
+                    panel.border = element_blank()
+                  ),
+                units = "mm",
+                width = 84,
+                height = 70,
+                # pointsize = 30,
+                dpi = 600)
+# 
+# 
+ggplot2::ggsave(filename = "1 PA Decline/sankey_change_ARTICLEA.png",
+                p_delta +
+                  theme_void() +
+                  theme(
+                    legend.position = "none",
+                    # panel.grid.major = element_blank(),
+                    # panel.grid.minor = element_blank(),
+                    # axis.text.y = element_blank(),
+                    # axis.title.y = element_blank(),
+                    axis.text.x = element_text(),
+                    text = element_text(size = 20),
+                    plot.title = element_blank(),
+                    # panel.background = element_rect(fill = "white"),
+                    plot.background = element_rect(fill="white"),
+                    panel.border = element_blank()
+                  ),
+                units = "mm",
+                width = 200,
+                height = 220,
+                # pointsize = 30,
+                dpi = 600)
+
+ggplot2::ggsave(filename = "1 PA Decline/sankey_change_ARTICLEA.pdf",
+                p_delta +
+                  theme_void() +
+                  theme(
+                    legend.position = "none",
+                    # panel.grid.major = element_blank(),
+                    # panel.grid.minor = element_blank(),
+                    # axis.text.y = element_blank(),
+                    # axis.title.y = element_blank(),
+                    axis.text.x = element_text(),
+                    text = element_text(size = 20),
+                    plot.title = element_blank(),
+                    # panel.background = element_rect(fill = "white"),
+                    plot.background = element_rect(fill="white"),
+                    panel.border = element_blank()
+                  ),
+                units = "mm",
+                width = 200,
+                height = 220,
+                # pointsize = 30,
+                dpi = 1200)
+
+# png(
+#   filename = "sankey_change_PhDDay.png",
+#   units = "mm",
+#   width = 100,
+#   height = 200,
+#   pointsize = 15,
+#   res = 300
+# )
+# p_delta +
+#   theme_minimal() +
+#   theme(
+#     legend.position = "none",
+#     panel.grid.major = element_blank(),
+#     panel.grid.minor = element_blank(),
+#     axis.text.y = element_blank(),
+#     axis.title.y = element_blank(),
+#     axis.text.x = element_text(size = 14, face = "bold"),
+#     plot.title = element_text(hjust = 0.5, vjust = 1, size = 30, face = "bold")
+#   )
+# dev.off()
+
+
+# png(
+#   filename = "sankey_change_PhDDay_min.png",
+#   units = "mm",
+#   width = 500,
+#   height = 500,
+#   pointsize = 15,
+#   res = 300
+# )
+# p_delta +
+#   theme_minimal() +
+#   theme(
+#     legend.position = "none",
+#     panel.grid.major = element_blank(),
+#     panel.grid.minor = element_blank(),
+#     axis.text.y = element_blank(),
+#     axis.title.y = element_blank(),
+#     axis.text.x = element_blank(),
+#     plot.title = element_blank(),
+#     panel.background = element_rect(fill = "transparent"),
+#     plot.background = element_rect(fill = "transparent", color = NA)
+#   )
+# dev.off()
+
+
+
+# png(
+#   filename = "sankey_change_ESOC23.png",
+#   units = "mm",
+#   width = 500,
+#   height = 500,
+#   pointsize = 60,
+#   res = 300
+# )
+# p_delta +
+#   theme_minimal() +
+#   theme(
+#     legend.position = "none",
+#     panel.grid.major = element_blank(),
+#     panel.grid.minor = element_blank(),
+#     axis.text.y = element_blank(),
+#     axis.title.y = element_blank(),
+#     axis.text.x = element_blank(),
+#     plot.title = element_blank(),
+#     panel.background = element_rect(fill = "transparent"),
+#     plot.background = element_rect(fill = "transparent", color = NA)
+#   )
+# dev.off()
+

1 PA Decline/calibration plot.R

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+# calibration plot
+
+
+ds <- openxlsx2::read_xlsx("1 PA Decline/Fra DDV/calibration_imp.xlsx")
+
+p <- ds |> split(ds$model) |> 
+  purrr::imap(\(.x,.i){
+    .x |> predtools::calibration_plot(obs="y",pred="pred")|> 
+      purrr::pluck("calibration_plot")+
+      ggplot2::ggtitle(.i)+
+      ggplot2::scale_x_continuous(breaks=seq(0,1,.25),limits=c(0,1))+
+      ggplot2::scale_y_continuous(breaks=seq(0,1,.25),limits=c(-.1,1.1))
+  })  |> 
+  patchwork::wrap_plots(ncol=2)
+
+
+ggplot2::ggsave(filename = "1 PA Decline/calibration_imp.pdf",
+                plot=p,
+                units = "mm",
+                width = 200,
+                height = 100,
+                # pointsize = 30,
+                dpi = 1200)
+  

1 PA Decline/coef plot.R

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+## Examples
+
+# stRoke::talos |>
+#   dplyr::mutate(across(tidyselect::starts_with("mrs"), as.numeric)) |>
+#   stRoke::generic_stroke(group = "rtreat", score = "mrs_6", variables = c("hypertension", "diabetes", "civil")) |>
+#   purrr::pluck(3)
+#
+# stRoke::talos |>
+#   dplyr::mutate(mrs_6_bin = as.numeric(mrs_6 < 1)) |>
+#   finalfit::or_plot(dependent = "mrs_6_bin", explanatory = c("hypertension", "diabetes", "civil"))
+
+
+# Consider utilising plotting like finalfit::or_plot
+
+
+## Sample data
+# df_coefs <- list(
+#   mrs_6 = stRoke::talos |>
+#     dplyr::select(tidyselect::all_of(c("mrs_6", "rtreat", "hypertension", "diabetes", "civil"))) |>
+#     lm(data = _, mrs_6 ~ .),
+#   mrs_1 = stRoke::talos |>
+#     dplyr::select(tidyselect::all_of(c("mrs_1", "rtreat", "hypertension", "diabetes", "civil"))) |>
+#     lm(data = _, mrs_1 ~ .)
+# ) |>
+#   lapply(gtsummary::tbl_regression) |>
+# purrr::map(function(.x) {
+#   .x |> purrr::pluck("table_body") |>
+#     dplyr::select(tidyselect::all_of(c("variable","estimate"))) |>
+#     na.omit()
+#   }) |> purrr::reduce(dplyr::full_join,by="variable") |>
+#   setNames(c("variable","increase","decrease"))
+
+get_coefs(path = here::here("1 PA Decline/Fra DDV/240624/pa_change_analyses.docx"),index.table = 2)
+
+
+source(here::here("1 PA Decline/dst import.R"))
+df_coefs_raw <- get_coefs(path = here::here("1 PA Decline/Fra DDV/240624/pa_change_analyses.docx"),index.table = 2) |>
+  dplyr::filter(variable != "(Intercept)") |>
+  dplyr::select(variable, hop_median, drop_median) |>
+  setNames(c("variable", "INCREASE", "DECREASE"))
+
+## Real work
+df_coefs <- df_coefs_raw |>
+  dplyr::mutate(dplyr::across(
+    tidyselect::all_of(c("INCREASE", "DECREASE")),
+    function(.x) {
+      # signif(
+        as.numeric(.x)#,
+      #   3
+      # )
+    }
+  )) |>
+  dplyr::mutate(variable = dplyr::if_else(variable == "Alcohol consumption above recommendations",
+    "High alcohol consumption", variable
+  ))|>
+  ## Important step to keep the data ordered for ggplot
+  (function(.y) {
+    .y |> dplyr::mutate(variable = factor(variable, levels = rev(.y$variable)))
+  })()
+
+## Highest ORs
+list(df_coefs[c(1,2)],df_coefs[c(1,3)]) |> 
+  setNames(names(df_coefs)[2:3]) |> 
+  purrr::map(function(.x){
+  .x |> 
+      setNames(c("var","val"))|> 
+      dplyr::mutate(sorting=abs(log(val))) |> 
+      dplyr::arrange(1-sorting) |> 
+      dplyr::mutate(dplyr::across(dplyr::where(is.numeric),~signif(.x,2))) |> 
+      head(5)
+})
+
+
+df_long <- df_coefs |>
+  tidyr::pivot_longer(cols = !tidyselect::matches("variable")) |>
+  dplyr::mutate(name = factor(name, levels = rev(unique(name))))
+
+
+cols <- c(
+  "#CE0045",
+  "#66c1a3"
+) # Lighter Midtrød
+
+
+create_log_tics <- function(data){
+  sort(round(unique(c(1/data,data)),2))
+}
+
+x.tics <- create_log_tics(c(.25, .4, .6, .8, 1))
+
+legend.title=""
+
+levels(df_long$name) <- c("OR for decrease",
+                          "OR for increase")
+
+p1 <- df_long |>
+  # dplyr::filter(name=="decrease") |>
+  ggplot2::ggplot(ggplot2::aes(x = log(value), y = variable, color = name, fill = name)) +
+  ggplot2::geom_vline(ggplot2::aes(xintercept = 0), linewidth = .5, linetype = "dashed") +
+  # ggplot2::geom_errorbarh(ggplot2::aes(xmax = boxCIHigh, xmin = boxCILow), size = .5, height =
+  #                  .2, color = "gray50") +
+  ggplot2::geom_point(ggplot2::aes(shape = name), size = 6)  + 
+  # ggplot2::coord_trans(x = scales:::exp_trans(10)) +
+  ggplot2::scale_x_continuous(
+    breaks = log(x.tics),
+    labels = x.tics,
+    limits = log(range(x.tics))
+  ) +
+  ggplot2::scale_color_manual(values = cols) +
+  ggplot2::scale_fill_manual(values = cols) +
+  ggplot2::scale_shape_manual(values=c(25,24)) +
+  ggplot2::theme_bw() +
+  ggplot2::theme(panel.grid.minor = ggplot2::element_blank(),
+                 # legend.title = ggplot2::element_text(""),
+                 legend.position = "bottom") +
+  ggplot2::ylab("") +
+  ggplot2::xlab("Odds ratio (log)") +
+  ggplot2::labs(shape=legend.title,
+                color=legend.title,
+                fill=legend.title)
+  
+# 
+# png(
+#   filename = here::here("1 PA Decline/coef_plot_change_ARTICLEA.png"),
+#   units = "mm",
+#   width = 300,
+#   height = 300,
+#   pointsize = 5,
+#   res = 300
+# )
+# p1 +
+#   # ggplot2::theme_minimal() +
+#   ggplot2::theme( 
+#     # legend.position = "none",
+#     # panel.grid.major = ggplot2::element_blank(),
+#     # panel.grid.minor = ggplot2::element_blank(),
+#     # axis.text.y = ggplot2::element_blank(),
+#     # axis.title.y = ggplot2::element_blank(),
+#     # axis.text.x = element_blank(),
+#     text = ggplot2::element_text(size = 25)#,
+#     # plot.title = element_text(),
+#     # panel.background = ggplot2::element_rect(fill = "transparent")#,
+#     # plot.background = ggplot2::element_rect(fill = "transparent", color = NA)
+#   )
+# dev.off()
+
+
+x.tics <- create_log_tics(c(.25, .6, 1))
+
+ggplot2::ggsave(
+  filename = here::here("1 PA Decline/coef_plot_change_ARTICLEA_facet.png"),
+  plot = p1 +
+    ggplot2::scale_x_continuous(
+      breaks = log(x.tics),
+      labels = x.tics,
+      limits = log(range(x.tics))
+    ) +
+    ggplot2::facet_wrap(facets = ggplot2::vars(name),ncol=2) +
+    # ggplot2::theme_minimal() +
+    ggplot2::theme( 
+      legend.position = "none",
+      # panel.grid.major = ggplot2::element_blank(),
+      # panel.grid.minor = ggplot2::element_blank(),
+      # axis.text.y = ggplot2::element_blank(),
+      # axis.title.y = ggplot2::element_blank(),
+      # axis.text.x = element_blank(),
+      text = ggplot2::element_text(size = 16)#,
+      # plot.title = element_text(),
+      # panel.background = ggplot2::element_rect(fill = "transparent")#,
+      # plot.background = ggplot2::element_rect(fill = "transparent", color = NA)
+    ),
+  units = "mm",
+  width = 200,
+  height = 200,
+  pointsize = 5,
+  dpi = 600
+)
+
+ggplot2::ggsave(
+  filename = here::here("1 PA Decline/coef_plot_change_ARTICLEA_facet.pdf"),
+  plot = p1 +
+    ggplot2::scale_x_continuous(
+      breaks = log(x.tics),
+      labels = x.tics,
+      limits = log(range(x.tics))
+    ) +
+    ggplot2::facet_wrap(facets = ggplot2::vars(name),ncol=2) +
+    # ggplot2::theme_minimal() +
+    ggplot2::theme( 
+      legend.position = "none",
+      # panel.grid.major = ggplot2::element_blank(),
+      # panel.grid.minor = ggplot2::element_blank(),
+      # axis.text.y = ggplot2::element_blank(),
+      # axis.title.y = ggplot2::element_blank(),
+      # axis.text.x = element_blank(),
+      text = ggplot2::element_text(size = 16)#,
+      # plot.title = element_text(),
+      # panel.background = ggplot2::element_rect(fill = "transparent")#,
+      # plot.background = ggplot2::element_rect(fill = "transparent", color = NA)
+    ),
+  units = "mm",
+  width = 200,
+  height = 200,
+  pointsize = 5,
+  dpi = 1200
+)
+
+
+
+# p1 <- df_long |>
+#   # dplyr::mutate(value=log10(value)
+#   #                              ) |>
+#   ggplot2::ggplot(ggplot2::aes(x = variable, y = log(value), fill = name)) +
+#   ggplot2::geom_bar(stat = "identity", position = ggplot2::position_dodge()) +
+#   ggplot2::coord_trans(y = scales:::exp_trans(10)) +
+#   ggplot2::scale_y_continuous(
+#     breaks = log10(c(.2, .4, .5, 1, 1.2, 1.4, 1.6, 2, 2.5)),
+#     labels = c(.2, .4, .5, 1, 1.2, 1.4, 1.6, 2, 2.5),
+#     limits = log10(c(0.09, 2.5))
+#   ) +
+#   ggplot2::geom_hline(yintercept = 0) +
+#   ggplot2::coord_flip() +
+#   ggplot2::scale_fill_manual(values = cols) +
+#   # REF: https://stackoverflow.com/a/22517219/21019325
+#   ggplot2::guides(fill = ggplot2::guide_legend(reverse = TRUE)) +
+#   ggplot2::ylab("OR (log))") +
+#   ggplot2::labs(
+#     fill = "Model" # ,
+#     # title = "Prediction models: increase and decrease after stroke",
+#     # subtitle = "Median coeficient after cross validation"
+#   ) +
+#   ggplot2::theme_classic(11) +
+#   ggplot2::theme(
+#     axis.title.x = ggplot2::element_text(),
+#     axis.title.y = ggplot2::element_blank(),
+#     axis.text.y = ggplot2::element_blank(),
+#     axis.line.y = ggplot2::element_blank(),
+#     axis.ticks.y = ggplot2::element_blank() # ,
+#     # legend.position = "none"
+#   )
+# p1
+
+# df_plot <- df_long |>
+#   dplyr::mutate(
+#     id = seq_len(dplyr::n()),
+#     dplyr::across(where(is.numeric), \(.i) signif(.i, digits = 2))
+#   ) |>
+#   (function(.x) {
+#     split(.x, .x[["variable"]]) |>
+#       purrr::map(function(.y) {
+#         .y |> dplyr::mutate(id = rev(id))
+#       }) |>
+#       dplyr::bind_rows()
+#   })() |>
+#   dplyr::mutate(id = rev(id)) |>
+#   (function(.z) {
+#     .z |> dplyr::mutate(var_label = variable |> (function(.x) {
+#       split(.z, .x) |>
+#         purrr::map(function(.y) {
+#           c("", unique(as.character(.y[[1]])))
+#         }) |>
+#         purrr::list_c()
+#     })())
+#   })() |>
+#   dplyr::mutate(val_label = paste("OR:", value))
+
+
+
+# df_plot <- df_long |>
+#   dplyr::mutate(
+#     id = seq_len(dplyr::n()),
+#     dplyr::across(where(is.numeric), \(.i) signif(.i, digits = 2))
+#   ) |>
+#   (function(.x) {
+#     split(.x, .x[["variable"]]) |>
+#       purrr::map(function(.y) {
+#         .y |> dplyr::mutate(id = rev(id))
+#       }) |>
+#       dplyr::bind_rows()
+#   })() |>
+#   dplyr::mutate(id = rev(id)) |>
+#   (function(.z) {
+#     .z |> dplyr::mutate(var_label = variable |> (function(.x) {
+#       split(.z, .x) |>
+#         purrr::map(function(.y) {
+#           c("", unique(as.character(.y[[1]])))
+#         }) |>
+#         purrr::list_c()
+#     })())
+#   })() |>
+#   dplyr::mutate(val_label = paste("OR:", value))
+# 
+# table_text_size <- 5
+# title_text_size <- 20
+# column_space <- c(0, .6)
+# 
+# t1 <- df_plot |> ggplot2::ggplot(ggplot2::aes(x = var, y = variable)) +
+#   ggplot2::annotate("text",
+#     x = column_space[1], y = df_plot$variable,
+#     label = df_plot[[5]], hjust = 0, size = table_text_size
+#   ) +
+#   # ggplot2::annotate("text",
+#   #   x = column_space[2], y = df_plot$id,
+#   #   label = df_plot[[2]], hjust = 0, size = table_text_size
+#   # ) +
+#   ggplot2::annotate("text",
+#     x = column_space[2], y = df_plot$variable,
+#     label = df_plot[[6]], hjust = 0, size = table_text_size
+#   ) +
+#   ggplot2::xlim(0, .8) +
+#   ggplot2::theme_classic(14) +
+#   ggplot2::theme(
+#     axis.title.x = ggplot2::element_text(colour = "white"),
+#     axis.text.x = ggplot2::element_text(colour = "white"),
+#     axis.title.y = ggplot2::element_blank(),
+#     axis.text.y = ggplot2::element_blank(),
+#     axis.ticks.y = ggplot2::element_blank(),
+#     line = ggplot2::element_blank()
+#   )
+# 
+# patchwork::wrap_plots(t1,
+#   p1,
+#   ncol = 2, widths = c(1, 1.5)
+# ) + patchwork::plot_annotation(title = "Prediction models: decrease and increase PA")
+# 
+# 
+# gridExtra::grid.arrange(t1,
+#   p1,
+#   ncol = 2,
+#   widths = c(1, 1.5),
+#   top = grid::textGrob("Prediction models: decrease and increase PA",
+#     x = 0.02, y = 0.2, gp = grid::gpar(fontsize = title_text_size),
+#     just = "left"
+#   )
+# )

1 PA Decline/data_format.R

@@ -0,0 +1,50 @@
+## Article 1 outcome group definition script
+## To be enriched from Statistics Denmark
+## 
+## Based on the ItMLiHSmar2022 course
+
+library(Hmisc)
+library(dplyr)
+# library(daDoctoR)
+library(tidyselect)
+
+# Setting final primary output from "pout"
+if (pout=="drop"){
+  X_tbl <- X_tbl|>
+    mutate(group=pase_drop_fac)
+  
+  # print(quantile(as.numeric(X_tbl$pase_0)))
+  # print(quantile(as.numeric(X_tbl$pase_6)))
+  # print(summary(X_tbl$pase_0_cut))
+  
+  X_tbl_f <- X_tbl|>
+    filter(pase_0_cut!=1)|>
+    select(-starts_with("pase_"))
+}
+
+if (pout=="hop"){
+  X_tbl <- X_tbl|>
+    mutate(group=pase_hop_fac)
+  
+  # print(quantile(as.numeric(X_tbl$pase_0)))
+  # print(quantile(as.numeric(X_tbl$pase_6)))
+  # print(summary(X_tbl$pase_0_cut))
+  
+  X_tbl_f <- X_tbl|>
+    filter(pase_6_cut!=1)|>
+    select(-starts_with("pase_"))
+}
+
+# Dropping non-complete for analysis
+Xy <- X_tbl_f|> 
+  na.omit()|> # Keeping only complete observations
+  select(-c(tci) # Left out of model as no present in drop-group
+  )|>
+  mutate(mrs_0=factor(ifelse(mrs_0==1,1,2))) # Sets binary mRS 0 to include in glmnet, 0 or above
+
+label(Xy) = as.list(var.labels[match(names(Xy), names(var.labels))])
+
+X<-dplyr::select(Xy,-c(group, -starts_with("pase_")) # Exclude primary outcome
+                 )
+y<-Xy$group
+

1 PA Decline/data_set.R

@@ -0,0 +1,216 @@
+## Article 1 data set definition
+## To be enriched from Statistics Denmark
+## 
+## Based on the ItMLiHSmar2022 course
+
+require(Hmisc)
+require(dplyr)
+# library(daDoctoR)
+require(tidyverse)
+require(patchwork)
+require(caret)
+require(glmnet)
+require(leaps)
+require(pROC)
+require(gt)
+require(gtsummary)
+require(glue)
+# library(ggdendro)
+require(corrplot)
+require(stRoke)
+
+## ====================================================================
+# Step 1: Import
+## ====================================================================
+
+if ("try-error" %in% class(t <- try(read.csv("/Volumes/Data/exercise/source/background.csv")))) {
+  export <-
+    read.csv(
+      "/Volumes/Data 1/exercise/source/background.csv",
+      colClasses = "character",
+      na.strings = c("NA", "", "unknown")
+    )
+} else if (!"try-error" %in% class(t)) {
+  export <-
+    read.csv(
+      "/Volumes/Data/exercise/source/background.csv",
+      colClasses = "character",
+      na.strings = c("NA", "", "unknown")
+    )
+}
+
+## ====================================================================
+# Step 2: Selection
+## ====================================================================
+
+
+export<-export[,c("pase_0",
+               "age",
+               "sex",
+               "civil",
+               "smoke_ever", 
+               "smoker",
+               "rtreat",
+               "alc",
+               "afli",
+               "hypertension",
+               "diabetes",
+               "mrs_0",
+               "nihss_c",
+               "thrombolysis",
+               "pad", 
+               "thrombechtomy", 
+               "ami", 
+               "tci", 
+               "pase_6")]
+
+## ====================================================================
+# Step 3: Formatting variables
+## ====================================================================
+
+export$diabetes[is.na(export$diabetes)]<-"no"
+export$diabetes[is.na(export$hypertension)]<-"no"
+export$thrombolysis[is.na(export$thrombolysis)]<-"no"
+export$thrombechtomy[is.na(export$thrombechtomy)]<-"no"
+export$pad[is.na(export$pad)]<-"no"
+export$ami[is.na(export$ami)]<-"no"
+# export$smoker_prev <- ifelse(export$smoker=="3","yes","no")
+export$smoker <- ifelse(export$smoker=="1","yes","no")
+export$smoker[is.na(export$smoker)] <- "no"
+# export$mrs_0[export$mrs_0==3]<-NA
+
+dta <- export %>% 
+  # as_tibble()%>%
+  mutate(any_rep=factor(ifelse(thrombolysis=="yes"|thrombechtomy=="yes","yes","no")), # If not noted, no therapy was received
+         male_sex= factor(ifelse(sex=="female","no","yes")),
+         # smoke_ever=factor(ifelse(smoke_ever=="never","no","yes")),
+         civil=factor(ifelse(civil=="partner","no","yes")), # Sets "yes" for not-cohabiting
+         rtreat=factor(ifelse(rtreat=="Placebo","no","yes")), # "Yes" receives active treatment
+         alc=factor(ifelse(alc=="more","yes","no")), # Yes for more than guideline
+         pase_0=as.numeric(pase_0),
+         pase_6=as.numeric(pase_6),
+         across(c("diabetes",
+                  "hypertension",
+                  "smoker",
+                  "afli",
+                  "pad",
+                  "ami",
+                  "tci",
+                  "mrs_0"),as.factor),
+         across(c("nihss_c",
+                  "age"),as.numeric )
+  )%>% 
+  select(-c(sex))
+
+
+## ====================================================================
+# Step 4: Defining outcome
+## ====================================================================
+
+## Changed to step 7
+## This is to perform proper quantile split based on actually included.
+
+## ====================================================================
+# Step 5: Ordering variables
+## ====================================================================
+
+vars <- c("age",
+          "male_sex",
+          "civil",
+          "pase_0",
+          "smoker",
+          "alc",
+          "afli",
+          "hypertension",
+          "diabetes",
+          "pad",
+          "ami",
+          "tci",
+          "mrs_0",
+          "nihss_c",
+          "any_rep",
+          "rtreat",
+          "pase_6")
+
+dta<-dta[vars]
+
+## ====================================================================
+# Step 6: Labeling
+## ====================================================================
+
+var.labels = c(age="Age",
+               male_sex="Male",
+               civil="Living alone",
+               pase_0="Pre-stroke PASE score",
+               pase_6="Six month PASE score",
+               smoker="Daily or occasinally smoking",
+               alc="More alcohol than recommendation",
+               afli="AFIB",
+               hypertension="Hypertension",
+               diabetes="Diabetes",
+               pad="PAD",
+               ami="Previous MI",
+               tci="Previous TIA",
+               mrs_0="Pre-stroke mRS  [-1]",
+               nihss_c="Acute NIHSS score",
+               thrombolysis="Acute thrombolysis",
+               thrombechtomy="Acute thrombechtomy",
+               any_rep="Any reperfusion therapy",
+               rtreat="Active trial treatment",
+               pase_drop_fac="PASE first quartile drop F",
+               pase_hop_fac="PASE first quartile hop F",
+               pase_0_cut="PASE 0 quartiles",
+               pase_6_cut="PASE 6 quartiles")
+
+
+
+## ====================================================================
+# Step 7: final data export
+## ====================================================================
+
+data_summary<-summary(dta)
+
+# Saving "old" factorised variables
+sel<-sapply(dta,is.factor)
+# Reformatting factors as 1/2 for analysis
+dta<-dta |> 
+  mutate(across(where(is.factor), as.numeric))|> # Turning factors into 1(no) or 2(yes) for model. Numbered alphabetically.
+  mutate(across(matches(colnames(dta)[sel]), as.factor),
+         across(starts_with("pase_"), as.numeric))
+
+# Filtering out non-PASE
+X_tbl<-dta |>
+  filter(!is.na(pase_0),!is.na(pase_6))
+
+nrow(X_tbl)
+
+# Defining possible outcome meassures. Keeping in df for characterisation
+X_tbl <- X_tbl|>
+  mutate(## Relative decline
+    pase_diff=(pase_0-pase_6),
+    pase_decl_rel = pase_diff/pase_0*100,
+    # pase_decl_rel_fac=factor(ifelse(pase_decl_rel>=rel_dif,"yes","no")),
+    ## Absolute decline
+    # pase_decl_abs_fac=factor(ifelse(pase_diff>=abs_dif,"yes","no")),
+    ## Drop
+    pase_0_cut=quantile_cut(as.numeric(pase_0),
+                            groups=4,
+                            group.names = c(as.character(1:4)),
+                            y=as.numeric(pase_0),
+                            ordered.f = TRUE,
+                            inc.outs = TRUE#, 
+                            # detail.lst=FALSE
+                            ),
+    pase_6_cut=quantile_cut(as.numeric(pase_6),
+                            groups=4,
+                            group.names = c(as.character(1:4)),
+                            y=as.numeric(pase_0),
+                            ordered.f = TRUE,
+                            inc.outs = TRUE#, 
+                            # detail.lst=FALSE
+                            ),
+    pase_drop_fac=factor(ifelse(pase_6_cut==1&pase_0_cut!=1,"yes","no")),
+    pase_hop_fac=factor(ifelse(pase_6_cut!=1&pase_0_cut==1,"yes","no")))
+
+Hmisc::label(X_tbl) = as.list(var.labels[match(names(X_tbl), names(var.labels))])
+

1 PA Decline/dst import.R

@@ -0,0 +1,83 @@
+#' Reads docx file and splits each table into list
+#'
+#' @param path file path
+#' @param data.type character vector. Could be "paragraph" or "table cell".
+#'
+#' @return
+#' @export
+#'
+#' @examples
+docx2ds <- function(path = here::here("data-raw/Deltagerliste Skirva 2024.docx"),
+                    data.type = "table cell", verbose = TRUE) {
+  # Ref: https://www.r-bloggers.com/2020/07/how-to-read-and-create-word-documents-in-r/
+  doc <- officer::read_docx(path)
+
+  content <- doc |> officer::docx_summary()
+
+  if (verbose) {
+    message("Content types in the current document are as follows:")
+    print(content$content_type |> unique())
+  }
+
+  table_cells <- content |> dplyr::filter(content_type %in% data.type)
+
+  # .x <- split(table_cells, table_cells$doc_index)[[4]]
+
+  split(table_cells, table_cells$doc_index) |> purrr::map(function(.x) {
+    table_data <- .x |>
+      dplyr::filter(!is_header) |>
+      dplyr::select(row_id, cell_id, text)
+
+    # split data into individual columns
+    splits <- split(table_data, table_data$cell_id)
+    splits <- lapply(splits, function(.y) .y$text)
+    splits <- splits |> 
+      purrr::keep(function(.y) length(.y)>1) 
+
+    # If a footer has been added, it is considered part of the first column,
+    # and will result in unequal col lengths.
+    # This solution does not handle merged cells
+    col_lengths <- lengths(splits)
+    
+    if (col_lengths[1] > col_lengths[2]){
+      splits[[1]] <- splits[[1]][seq_len(col_lengths[2])]
+    }
+    
+    # combine columns back together in wide format
+    table_result <- splits |> 
+      dplyr::bind_cols()
+    
+    # get table headers
+    cols <- .x |> dplyr::filter(is_header)
+    names(table_result) <- cols$text
+    table_result
+  })
+}
+
+get_coefs <- function(path, 
+                      index.table = 1) {
+  data = docx2ds(
+    path = path
+  )
+  
+  data |>
+    purrr::pluck(index.table) |>
+    setNames(c(
+      "variable",
+      lapply(c("drop", "hop"),
+        paste,
+        c("median", "mean"),
+        sep = "_"
+      ) |>
+        purrr::list_c()
+    )) |>
+    dplyr::select(variable, tidyselect::ends_with("median")) #|>
+  # dplyr::mutate(dplyr::across(tidyselect::ends_with("median"),~as.numeric))
+  # setNames(c("variable","decrease","increase"))
+}
+
+gtsummary2docx <- function(data, path) {
+  data |>
+    gtsummary::as_flex_table() |>
+    flextable::save_as_docx(path = path)
+}

1 PA Decline/flowchart.R

@@ -0,0 +1,48 @@
+create_flowchart <- function(data, export.path = NULL) {
+  out <- data |> 
+    dplyr::select(pase_0,pase_4) |> 
+    dplyr::mutate(id= dplyr::row_number(),
+                  exclude=is.na(pase_0)|is.na(pase_4),
+                  exclude_reason=factor(dplyr::case_when(
+      is.na(pase_0) ~ "Pre-stroke PASE missing",
+      is.na(pase_4) ~"Post-stroke PASE missing"
+    ),levels=c( "Pre-stroke PASE missing","Post-stroke PASE missing")),
+    ) |> 
+    consort::consort_plot(
+    orders = c(
+      id = "Complete TALOS cohort",
+      exclude_reason = "Excluded",
+      id = "Main dataset"
+    ),
+    side_box = c("exclude_reason"),
+    labels = c(
+      "1" = "Identification",
+      "2" = "Inclusion",
+      "3" = "Complete data"
+    )
+  )
+  
+  if (!is.null(export.path)) {
+    out |> export_consort_dot(path = export.path)
+  } else {
+    plot(out)
+  }
+}
+
+source(here::here("2 Longterm/data.R"))
+# The original functions from DST unedited
+source(here::here("1 PA Decline/Fra DDV/functions200411.R"))
+source(here::here("1 PA Decline/Fra DDV/functions240418.R"))
+# Modified functions to overwrite original
+source(here::here("R/functions.R"))
+
+df <- df_ddv |>
+  dplyr::tibble() |>
+  dplyr::mutate_all(as.character) |>
+  ready_clin() |>
+  data_formatting()|>
+  get_vars(c("clin","lifestyle","ses", "assess.pred"))  |> 
+  dplyr::mutate(exclude=ifelse(is.na(pase_0)|is.na(pase_4),"Excluded","Included"),
+                age=as.numeric(age))
+
+df |> create_flowchart()

1 PA Decline/grouped table rows.qmd

@@ -0,0 +1,59 @@
+---
+title: "nicer tables"
+format: docx
+editor: visual
+---
+
+## Tables
+
+```{r}
+library(gtsummary)
+library(dplyr)
+packageVersion("gtsummary")
+
+trial%>%
+  select(age, stage, grade)%>%
+  tbl_summary()%>%
+  modify_table_body(
+    ~.x %>% 
+      
+      # add your variable
+      rbind(
+        tibble(
+          variable="Demographics",
+          var_type=NA,
+          var_label = "Demographics",
+          row_type="label",
+          label="Demographics",
+          stat_0= NA))%>% # expand the components of the tibble as needed if you have more columns
+      
+      # can add another one
+      rbind(
+        tibble(
+          variable="Tumor characteristics",
+          var_type=NA,
+          var_label = "Tumor characteristics",
+          row_type="label",
+          label="Tumor characteristics",
+          stat_0= NA))%>%  
+      
+      # specify the position you want these in
+      
+      arrange(factor(variable, levels=c("Demographics",
+                                        "age",
+                                        "Tumor characteristics",
+                                        "stage",
+                                        "grade"))))%>%
+  
+  # and you can then indent the actual variables
+  modify_column_indent(columns=label, rows=variable%in%c("age",
+                                                         "stage",
+                                                         "grade"))%>%
+  
+  # and double indent their levels
+  modify_column_indent(columns=label, rows= (variable%in%c("stage",
+                                                           "grade") 
+                                             & row_type=="level"),
+                       double_indent=T)
+
+```

1 PA Decline/regular_fun.R

@@ -0,0 +1,117 @@
+## ItMLiHSmar2022
+## regular_fun.R, child script
+## Regularisation model building function
+## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk
+## 
+## Now modified to use in publication
+## 
+
+regular_fun<-function(X,y,K,lambdas,alpha){
+n<-nrow(X)
+set.seed(321)
+
+# Using caret function to ensure both levels represented in all folds
+c<-createFolds(y=y, k = K, list = FALSE, returnTrain = TRUE) 
+
+B<-yhatTestProbKeep<-list()
+accTrain<-accTest<-err_train<-err_test<-auc_train<-auc_test<-matrix(nrow = K,ncol = length(lambdas))
+
+catinfo<-levels(y)
+
+cMatTrain<-cMatTest<-table(true=factor(c(0,0),levels=catinfo),pred=factor(c(0,0),levels=catinfo))
+
+
+## Iterate over partitions
+for (idx1 in 1:K){
+  
+  # Status
+  cat('Processing fold', idx1, 'of', K,'\n')
+  
+  # idx1=1
+  # Get training- and test sets
+  I_train = c!=idx1 ## Creating selection vector of TRUE/FALSE
+  I_test = !I_train
+  
+  Xtrain = X[I_train,]
+  ytrain = y[I_train]
+  Xtest = X[I_test,]
+  ytest = y[I_test]
+  
+  
+  ## Model matrices for glmnet
+  ## Using the complicated approach not to include first level.
+  # Xmat.train<-model.matrix(~ .-1, data=Xtrain, 
+  #              contrasts.arg = lapply(Xtrain[,sapply(Xtrain, is.factor)], 
+  #                                     contrasts, contrasts=T))
+  # Xmat.test<-model.matrix(~ .-1, data=Xtest, 
+  #                contrasts.arg = lapply(Xtest[,sapply(Xtest, is.factor)], 
+  #                                       contrasts, contrasts=T))
+  
+  # Xmat.train<-model.matrix(~.-1,Xtrain)
+  # Xmat.test<-model.matrix(~.-1,Xtest)
+  
+  # Weights
+  ytrain_weight<-as.vector(1 - (table(ytrain)[ytrain] / length(ytrain)))
+  # ytest_weight<-as.vector(1 / (table(ytest)[ytest] / length(ytest)))
+  
+  # Fit regularized linear regression model
+  mod<-glmnet(Xtrain, ytrain, 
+              alpha = alpha,        ## Alpha = 1 for lasso
+              lambda = lambdas, ## Setting lambdas
+              standardize = TRUE, ## Scales and centers
+              weights = ytrain_weight,
+              family = "binomial"
+              )
+  
+  # Keep coefficients for plot
+  B[[idx1]] <- as.matrix(coef(mod))
+  
+  # Iterate over regularization strengths to compute training- and test
+  # errors for individual regularization strengths.
+  for (idx2 in 1:length(lambdas)){
+    # idx2=1
+    
+    # Predict
+    yhatTrainProb<-predict(mod, 
+                           s = lambdas[idx2], 
+                           newx = data.matrix(Xtrain),
+                           type = "response"
+    )
+    
+    yhatTestProb<-predict(mod, 
+                          s = lambdas[idx2], 
+                          newx = data.matrix(Xtest),
+                          type = "response"
+    )
+    
+    # Compute training and test error
+    yhatTrain = round(yhatTrainProb)
+    yhatTest = round(yhatTestProb)
+    
+    # Make predictions categorical again (instead of 0/1 coding)
+    yhatTrainCat = factor(round(yhatTrainProb),levels=c("0","1"),labels=catinfo,ordered = TRUE)
+    yhatTestCat = factor(round(yhatTestProb),levels=c("0","1"),labels=catinfo,ordered = TRUE)
+    
+    # Evaluate classifier performance
+    # Accuracy
+    # accTrain[idx1,idx2] <- sum(yhatTrainCat==ytrain)/length(ytrain)
+    # accTest [idx1,idx2] <- sum(yhatTestCat==ytest)/length(ytest)
+    # #
+    # # Error rate
+    # err_train[idx1,idx2] =  1 - accTrain[idx1,idx2]
+    # err_test [idx1,idx2] =   1 - accTest[idx1,idx2]
+    
+    # AUROC
+    suppressMessages(
+      auc_train[idx1,idx2]<-auc(ytrain, yhatTrainCat))
+    suppressMessages(
+      auc_test [idx1,idx2]<-auc(ytest, yhatTestCat))
+    
+    # Compute confusion matrices
+    cMatTrain = cMatTrain + table(true=ytrain,pred=yhatTrainCat)
+    cMatTest = cMatTest + table(true=ytest,pred=yhatTestCat)
+  }
+}
+ls<-list(mod=mod,B=B,auc_train=auc_train,auc_test=auc_test,cMatTrain=cMatTrain,cMatTest=cMatTest)
+return(ls)
+}

1 PA Decline/regularisation_steps.R

@@ -0,0 +1,150 @@
+## ItMLiHSmar2022
+## regularisation_steps.R, child script
+## Regularised model building and analysation for assignment
+## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk
+## 
+## Now modified to use in publication
+## 
+
+## ====================================================================
+## Step 0: data import and wrangling
+## ====================================================================
+
+setwd("/Users/au301842/PhysicalActivityandStrokeOutcome/1 PA Decline/")
+
+# source("data_format.R")
+y1<-factor(as.integer(y)-1) ## Outcome is required to be factor of 0 or 1.
+
+
+## ====================================================================
+## Step 1: settings
+## ====================================================================
+
+## Folds
+K=10
+set.seed(3)
+c<-caret::createFolds(y=y, 
+                      k = K, 
+                      list = FALSE, 
+                      returnTrain = TRUE) # Foldids for alpha tuning
+
+## Defining tuning parameters
+lambdas=2^seq(-10, 5, 1)
+alphas<-seq(0,1,.1)
+
+## Weights for models
+weighted=TRUE
+if (weighted == TRUE) {
+  wght<-as.vector(1 - (table(y)[y] / length(y)))
+} else {
+  wght <- rep(1, nrow(y))
+}
+
+
+## Standardise numeric
+## Centered and 
+
+
+
+## ====================================================================
+## Step 2: all cross validations for each alpha
+## ====================================================================
+
+library(furrr)
+library(purrr)
+library(doMC)
+registerDoMC(cores=6)
+
+# Nested CVs with analysis for all lambdas for each alpha
+# 
+set.seed(3)
+cvs <- future_map(alphas, function(a){
+  cv.glmnet(model.matrix(~.-1,X),
+            y1,
+            weights = wght,
+            lambda=lambdas, 
+            type.measure = "deviance", # This is standard measure and recommended for tuning
+            foldid = c, # Per recommendation the folds are kept for alpha optimisation
+            alpha=a,
+            standardize=TRUE,
+            family=quasibinomial,
+            keep=TRUE) # Same as binomial, but not as picky
+})
+
+## ====================================================================
+# Step 3: optimum lambda for each alpha
+## ====================================================================
+
+
+# For each alpha, lambda is chosen for the lowest meassure (deviance)
+each_alpha <- sapply(seq_along(alphas), function(id) {
+  each_cv <- cvs[[id]]
+  alpha_val <- alphas[id]
+  index_lmin <- match(each_cv$lambda.min, 
+                      each_cv$lambda)
+  c(lamb = each_cv$lambda.min, 
+    alph = alpha_val,
+    cvm = each_cv$cvm[index_lmin])
+})
+
+# Best lambda
+best_lamb <- min(each_alpha["lamb", ])
+
+# Alpha is chosen for best lambda with lowest model deviance, each_alpha["cvm",]
+best_alph <- each_alpha["alph",][each_alpha["cvm",]==min(each_alpha["cvm",]
+                                                         [each_alpha["lamb",] %in% best_lamb])]
+
+## https://stackoverflow.com/questions/42007313/plot-an-roc-curve-in-r-with-ggplot2
+p_roc<-roc.glmnet(cvs[[1]]$fit.preval, newy = y)[[match(best_alph,alphas)]]|> # Plots performance from model with best alpha
+  ggplot(aes(FPR,TPR)) + 
+  geom_step() +
+  coord_cartesian(xlim=c(0,1), ylim=c(0,1)) +
+  geom_abline()+
+  theme_bw()
+
+## ====================================================================
+# Step 4: Creating the final model
+## ====================================================================
+
+source("regular_fun.R") # Custom function
+optimised_model<-regular_fun(X,y1,K,lambdas=best_lamb,alpha=best_alph) 
+# With lambda and alpha specified, the function is just a k-fold cross-validation wrapper, 
+# but keeps model performance figures from each fold.
+
+list2env(optimised_model,.GlobalEnv)
+# Function outputs a list, which is unwrapped to Env.
+# See source script for reference.
+
+## ====================================================================
+# Step 5: creating table of coefficients for inference
+## ====================================================================
+
+Bmatrix<-matrix(unlist(B),ncol=10)
+Bmedian<-apply(Bmatrix,1,median)
+Bmean<-apply(Bmatrix,1,mean)
+
+reg_coef_tbl<-tibble(
+  name = c("Intercept",Hmisc::label(X)),
+  medianX = round(Bmedian,5),
+  ORmed = round(exp(Bmedian),5),
+  meanX = round(Bmean,5),
+  ORmea = round(exp(Bmean),5))%>%
+  # arrange(desc(abs(medianX)))%>%
+  gt()
+
+## ====================================================================
+# Step 6: plotting predictive performance
+## ====================================================================
+
+reg_cfm<-confusionMatrix(cMatTest)
+reg_auc_sum<-summary(auc_test[,1])
+
+## ====================================================================
+# Step 7: Packing list to save in loop
+## ====================================================================
+
+ls[[i]] <- list("RegularisedCoefs"=reg_coef_tbl,
+                "bestA"=best_alph,
+                "bestL"=best_lamb,
+                "ConfusionMatrx"=reg_cfm,
+                "AUROC"=reg_auc_sum)

1 PA Decline/repeated measures.R

@@ -0,0 +1,29 @@
+# ds <- readr::read_csv(here::here("/Volumes/Data/REDCap/DDV/talos_ddv.csv"))
+
+
+# Tun first 3 lines of code in 00_master.R
+
+# Label attributes are removed then pivoted to long
+df <- purrr::map(X_tbl,\(.x){
+  # browser()
+  class(.x) <- class(.x)[-1]
+  .x
+}) |> 
+  dplyr::bind_cols() |> 
+  dplyr::mutate(id=dplyr::row_number()) |> 
+  dplyr::select(id,dplyr::everything()) |> 
+  tidyr::pivot_longer(c("pase_0","pase_6"),names_to = "time",values_to = "pase") |> 
+  dplyr::mutate(time = as.numeric(factor(time))-1)
+  
+lme4::lmer(formula = pase~age+male_sex+hypertension+diabetes+nihss_c+(1|id),data = df) |> 
+  gtsummary::tbl_regression()
+
+summary(df$male_sex)
+
+df |> 
+  dplyr::mutate(dplyr::across(c("id","hypertension","diabetes","time"),\(.x)factor(.x)),
+                female=male_sex==1) |> 
+    (\(.x){
+      mmrm::mmrm(formula = pase~age+female+hypertension+diabetes+nihss_c+us(time|id),data=.x)
+    })() |> gtsummary::tbl_regression()
+

1 PA Decline/sankey individual.R

@@ -0,0 +1,56 @@
+ds <- readr::read_csv(here::here("/Volumes/Data/REDCap/DDV/talos_ddv.csv"))
+
+df_raw <- ds |>
+  dplyr::filter(!pase_score_missings_0, !pase_score_missings_4) |>
+  dplyr::transmute(
+    id = dplyr::row_number(),
+    pase_0 = pase_score_sum_0,
+    pase_4 = pase_score_sum_4,
+    pase_0_cut = as.numeric(stRoke::quantile_cut(
+      x = pase_0,
+      groups = 4,
+      group.names = paste0(1:4)
+    )),
+    pase_6_cut = as.numeric(stRoke::quantile_cut(
+      x = pase_4,
+      y = pase_0,
+      groups = 4,
+      inc.outs = TRUE,
+      group.names = paste0(1:4)
+    )),
+    pase_diff = pase_4 - pase_0,
+    pase_diff_rel = pase_diff / pase_0,
+    pase_0_rank = rank(pase_0, ties.method = "first"),
+    pase_4_rank = rank(pase_4, ties.method = "first"),
+    change = dplyr::case_when(
+      pase_0_cut %in% 2:4 & pase_6_cut == 1 ~ "drop",
+      pase_6_cut %in% 2:4 & pase_0_cut == 1 ~ "hop",
+      pase_0_cut %in% 2:4 & pase_6_cut %in% 2:4 ~ "hh",
+      pase_0_cut %in% 1 & pase_6_cut == 1 ~ "ll"
+    ),
+    change_any = factor(dplyr::case_when(
+      pase_6_cut > pase_0_cut ~ "hop",
+      pase_6_cut < pase_0_cut ~ "drop",
+      pase_0_cut %in% 2:4 & pase_6_cut %in% 2:4 ~ "hh",
+      pase_0_cut %in% 1 & pase_6_cut == 1 ~ "ll"
+    )),
+    change_rel = factor(dplyr::case_when(
+      pase_diff_rel > .5 ~ "hop",
+      pase_diff_rel < -.5 ~ "drop",
+      .default = "stat"
+    ))
+  )
+
+df_raw |> skimr::skim()
+
+df_long <- df_raw |>
+  dplyr::select(pase_0_rank, pase_4_rank, change_rel,id) |>
+  tidyr::pivot_longer(dplyr::starts_with("pase_"), names_to = "time", values_to = "pase")
+
+df_long <- df_raw |>
+  dplyr::select(pase_0, pase_4, change_rel,id) |>
+  tidyr::pivot_longer(dplyr::starts_with("pase_"), names_to = "time", values_to = "pase")
+
+ggplot(df_long, aes(x = time, y = pase,group=id,colour = change_rel)) +
+  geom_line()+
+  facet_wrap(~change_rel)

1 PA Decline/sankey.R

@@ -0,0 +1,155 @@
+
+# source("1 PA Decline/data_format.R")
+
+# NEW QUARTILES
+
+df <- X_tbl |> select(pase_0_cut,pase_6_cut)
+
+df$change <- factor(ifelse(
+  df$pase_0_cut %in% 2:4 & df$pase_6_cut == 1,
+  "drop",
+  ifelse(
+    df$pase_6_cut %in% 2:4 & df$pase_0_cut == 1,
+    "hop",
+    "no"
+  )))
+
+
+# Visuals - sankey
+# https://stackoverflow.com/questions/50395027/beautifying-sankey-alluvial-visualization-using-r
+
+
+## Painting
+
+df <- df |> count(pase_0_cut,pase_6_cut,change)
+
+
+lbs0 <-
+  c(
+    paste0("1st \n(n=", sum(df$n[df$pase_0_cut == "1"]), ")"),
+    paste0("2nd \n(n=", sum(df$n[df$pase_0_cut == "2"]), ")"),
+    paste0("3rd \n(n=", sum(df$n[df$pase_0_cut == "3"]), ")"),
+    paste0("4th \n(n=", sum(df$n[df$pase_0_cut == "4"]), ")")
+  )
+
+
+lbs6 <-
+  c(
+    paste0("1st \n(n=", sum(df$n[df$pase_6_cut == "1"]), ")"),
+    paste0("2nd \n(n=", sum(df$n[df$pase_6_cut == "2"]), ")"),
+    paste0("3rd \n(n=", sum(df$n[df$pase_6_cut == "3"]), ")"),
+    paste0("4th \n(n=", sum(df$n[df$pase_6_cut == "4"]), ")")
+  )
+
+
+levels(df$pase_0_cut)<-lbs0[1:length(levels(df$pase_0_cut))]
+levels(df$pase_6_cut)<-lbs6[1:length(levels(df$pase_6_cut))]
+
+df$pase_0_cut <- factor(df$pase_0_cut, levels=rev(levels(df$pase_0_cut)))
+df$pase_6_cut <- factor(df$pase_6_cut, levels=rev(levels(df$pase_6_cut)))
+
+df$change <- factor(df$change, levels=c("no", "drop", "hop"))
+
+
+hops <- "#66c1a3"  # grey
+# drops <- "#990033"  # Midtrød
+drops <- "#CE0045" #Lighter Midtrød
+nos   <- "grey90" # Light grey
+
+# border <- "#00596B"
+# box <- "#008099"
+
+border <- "#EA571D"
+box <- "#1E4B66"
+
+
+cls <- c(nos, drops, hops)
+
+alpha <- 0.7
+          
+library(ggalluvial)
+
+p_delta <- ggplot(df,aes(y = n, axis1 = pase_0_cut, axis2 = pase_6_cut)) +
+  geom_alluvium(
+    aes(fill = change, color = change),
+    width = 1 / 16,
+    alpha = alpha,
+    knot.pos = 0.4
+  ) +
+  geom_stratum(aes(size=10),width = 1 / 4,
+               fill = box,
+               color = border) +
+  geom_text(stat = "stratum", aes(label = after_stat(stratum)), colour = "white", size = 20) +
+  scale_x_continuous(breaks = 1:2,
+                     labels = c("Pre-stroke\nquartile", "Six months\nquartile"))     +
+  scale_fill_manual(values  = cls) +
+  scale_color_manual(values = cls) +
+  ggtitle("Change in PA\nafter stroke")
+
+
+# plotly::ggplotly(p_delta)
+
+png(
+  filename = "sankey_change_PhDDay.png",
+  units = "mm",
+  width = 100,
+  height = 200,
+  pointsize = 15,
+  res = 300
+); p_delta +
+  theme_minimal() +
+  theme(
+    legend.position = "none",
+    panel.grid.major = element_blank(),
+    panel.grid.minor = element_blank(),
+    axis.text.y = element_blank(),
+    axis.title.y = element_blank(),
+    axis.text.x = element_text(size = 14, face = "bold"),
+    plot.title = element_text(hjust = 0.5, vjust = 1, size = 30, face = "bold")
+  ); dev.off()
+          
+
+png(
+  filename = "sankey_change_PhDDay_min.png",
+  units = "mm",
+  width = 500,
+  height = 500,
+  pointsize = 15,
+  res = 300
+); p_delta+
+  theme_minimal() +
+  theme(
+    legend.position = "none",
+    panel.grid.major = element_blank(),
+    panel.grid.minor = element_blank(),
+    axis.text.y = element_blank(),
+    axis.title.y = element_blank(),
+    axis.text.x = element_blank(),
+    plot.title = element_blank(),
+    panel.background = element_rect(fill='transparent'), 
+    plot.background = element_rect(fill='transparent', color=NA)
+  ); dev.off()
+
+
+
+png(
+  filename = "sankey_change_ESOC23.png",
+  units = "mm",
+  width = 500,
+  height = 500,
+  pointsize = 60,
+  res = 300
+); p_delta +
+  theme_minimal() +
+  theme(
+    legend.position = "none",
+    panel.grid.major = element_blank(),
+    panel.grid.minor = element_blank(),
+    axis.text.y = element_blank(),
+    axis.title.y = element_blank(),
+    axis.text.x = element_blank(),
+    plot.title = element_blank(),
+    panel.background = element_rect(fill='transparent'), 
+    plot.background = element_rect(fill='transparent', color=NA)
+  ); dev.off()
+

1 PA Decline/standardise.R

@@ -0,0 +1,41 @@
+## ItMLiHSmar2022
+## standardise.R, child script
+## Data standardisation, returns list
+## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk
+
+standardise<-function(train,test,type){
+  # From:
+  # https://datascience.stackexchange.com/questions/13971/standardization-normalization-test-data-in-r
+  
+  sel<-sapply(Xtrain,is.numeric) # Deciding which to stadardise (only numeric)
+  cnm<-colnames(Xtrain) # Saving column names for ordering
+  
+  # Subsetting
+  
+  ## Data to treat
+  train.tr<-train[,sel]
+  test.tr<-test[,sel]
+  
+  ## Data to save
+  train.sv<-train[,!sel]
+  test.sv<-test[,!sel]
+  
+  # Calculate mean and SD of train data
+  trainMean <- sapply(train.tr,mean)
+  trainSd <- sapply(train.tr,sd)
+  
+  if (type=="c"){
+    ## centered
+    norm.trainData<-sweep(train.tr, 2L, trainMean) # using the default "-" to subtract mean column-wise   
+    norm.testData<-sweep(test.tr, 2L, trainMean) # using the default "-" to subtract mean column-wise   
+  }
+ 
+  if (type=="cs"){
+    ## centered AND scaled (Z-score standardisation)
+    norm.trainData<-sweep(sweep(train.tr, 2L, trainMean), 2, trainSd, "/")
+    norm.testData<-sweep(sweep(test.tr, 2L, trainMean), 2, trainSd, "/")
+  }
+  return(list(XtrainSt=cbind(norm.trainData,train.sv)[,cnm], # Reordering columns to original
+              XtestSt=cbind(norm.testData,test.sv)[,cnm]))
+}
+

1 PA Decline/summaries.qmd

@@ -0,0 +1,109 @@
+---
+title: "Article A: Final renders"
+format: docx
+editor: visual
+---
+
+```{r}
+# ds <- readr::read_csv(here::here("/Volumes/Data/REDCap/DDV/talos_ddv.csv"))
+source(here::here("2 Longterm/data.R"))
+# The original functions from DST unedited
+source(here::here("1 PA Decline/Fra DDV/functions200411.R"))
+source(here::here("1 PA Decline/Fra DDV/functions240418.R"))
+# Modified functions to overwrite original
+source(here::here("R/functions.R"))
+```
+
+Files and resources used for article ready data:
+
+-   "2 Longterm/data.R"
+
+-   "R/functions240319.R"
+
+```{r}
+# ds |> finalfit::missing_plot()
+```
+
+On handling missings: https://finalfit.org/articles/missing.html
+
+```{r}
+# unique(df_long$variable)
+```
+
+```{r}
+pred_data <- df_ddv |>
+  dplyr::tibble() |>
+  dplyr::mutate_all(as.character) |>
+  ready_clin() |>
+  data_formatting() |>
+  prediction_ready() |>
+  dplyr::mutate(
+    dplyr::across(
+      c(
+        tidyselect::starts_with("pase_"),
+        "age"
+      ),
+      as.numeric
+    )
+  )
+
+pred_data |> labelling_data() |> readr::write_rds("labelled_test.rds")
+
+skimr::skim(pred_data)
+
+wilcox.test(pred_data$pase_0, pred_data$pase_4, paired = TRUE)
+```
+
+```{r}
+pred_data |>
+  true_pred_sum_plot() |>
+  gtsummary::as_gt() |>
+  # add_var_groups_gt()|>
+  gt::gtsave(filename = here::here("1 PA Decline/table1.docx"))
+system2("open",here::here("'1 PA Decline/table1.docx'"))
+# gtsummary2docx(path=here::here("1 PA Decline/table1.docx"))
+```
+
+```{r}
+pred_data |>
+  dplyr::mutate(reg_female = dplyr::if_else(reg_female, "Female", "Male")) |>
+  pase_cutter(drop.pase = FALSE) |>
+  dplyr::select(pase_change, dplyr::everything()) |>
+  summary_tblone(by = "reg_female", missing = "no") |>
+  gtsummary::modify_column_hide("stat_0") |>
+  gtsummary::add_p() |>
+  gtsummary::bold_p() |>
+  micRo::mask_micro_summary() |>
+  gtsummary::as_gt() |>
+  gt::gtsave(filename = here::here("1 PA Decline/table_bysex.docx"))
+```
+
+```{r}
+skimr::skim(pred_data)
+```
+
+## Excluded patients
+
+```{r}
+df_ddv |>
+  dplyr::tibble() |>
+  dplyr::mutate_all(as.character) |>
+  ready_clin() |>
+  data_formatting()|>
+  get_vars(c("clin","lifestyle","ses", "assess.pred"))  |> 
+  dplyr::mutate(exclude=ifelse(is.na(pase_0)|is.na(pase_4),"Excluded","Included"),
+                age=as.numeric(age),
+                pase_0=as.numeric(pase_0),
+                pase_4=as.numeric(pase_4)
+                )|> 
+  # dplyr::select(-pase_0,-pase_4)  |> 
+  #dplyr::select(exclude,soc_status_nowork, fam_indk_hl, edu_level_hl)|> 
+  gtsummary::tbl_summary(by=exclude, missing = "ifany") |> 
+  gtsummary::add_p() |> 
+  fix_labels() |> 
+  gtsummary::as_gt() |> 
+  gt::gtsave("1 PA Decline/summary_by_missing.docx")
+#|> 
+ # mask_micro_summary(micro.n = 5)
+
+```

1 PA Decline/table mods.R

@@ -0,0 +1,41 @@
+source(here::here("1 PA Decline/dst import.R"))
+ls_tbl <- docx2ds(
+  path = "/Users/au301842/Library/CloudStorage/OneDrive-Personal/Research/PhD/1 Change in PA/Manuscript/Manuscript_Change in PA_v2_10_IJS.docx"
+)
+
+ls <- ls_tbl |> purrr::pluck(3) |>
+  (\(.x){
+    .x[-c(1:3,nrow(.x)),]
+  })()
+
+ls |> setNames(1:5) |> 
+  dplyr::mutate(dplyr::across(2:5,as.numeric)) |> 
+  gt::gt() |> gt::fmt_number(n_sigfig=3) |> gt::gtsave("modtable3.docx")
+
+
+
+ls |> setNames(1:5) |> 
+  dplyr::mutate(dplyr::across(2:5, \(.x){
+    as.character(sprintf('%#.3g', as.numeric(.x)))
+    })) |> kableExtra::kable()
+  
+
+## Splitting by leveled variables (expanded view with all levels)
+n <- 0
+for (i in seq_len(nrow(ls))){
+  if (ls[[2]][i]!=""){
+    n[i]<-n[length(n)]+1
+  } else {
+    n[i] <- n[length(n)]
+  }
+}
+
+split(ls,n) |> lapply(function(.x){
+
+  if ("  TRUE" %in% .x[[1]]){
+    cbind(.x[1,1:2],.x[3,3:5],.x[1,6],.x[3,7:9])
+  } else {
+    .x
+  }
+}) |> dplyr::bind_rows() |>
+  gt::gt()