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