# Code from: cont_pase_sens.qmd
targets::tar_config_set(store = here::here("_targets"))
source(here::here("R/functions.R"))
source(here::here("R/glmnet-reg.R"))
library(targets)
library(tidyverse)
tbl_cont_0 <- targets::tar_read("df_all_data_formatted")|> 
  dplyr::filter(!is.na(pase_0),!is.na(pase_4)) |> 
  # pase_cutter(drop.nas = TRUE) |>
  events_ready(v.groups=c("clin","lifestyle.events","ses", "assess.events","quartiles"))  |>
  dplyr::select(dplyr::all_of(c("pase_0", "age", "reg_female", "nihss_0", "reg_trombolyse", 
"reg_trombektomi", "rtreat_placebo", "reg_alone", "reg_smoker", 
"reg_more_alc", "reg_hyperten", "reg_diabetes", "reg_atriefli", 
"reg_ami", "soc_status_nowork", "fam_indk_hl", "edu_level_hl", 
"who_4", "mdi_4", "mfi_gen_4", "mrs_4_above1", "time", "status"
))) |>
  (\(data){
    # c("pase_0","pase_4") |> 
    #   purrr::map(\(exp){
        list("Univariable"=cox_regression(data=data,all.vars = FALSE, use.strata = FALSE, outcome.var = "pase_0"),
             "Multivariable"=cox_regression(data=data,all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_0")) |> 
          purrr::map(\(.x){
            .x |> gtsummary::tbl_regression(exponentiate=TRUE) |> gtsummary::add_nevent()|> 
  fix_labels()
          }) |> 
          tbl_merged_named()
      # })
    })()   |>
      gtsummary::modify_table_body( ~ filter(.x, variable == "pase_0"))
tbl_cont_4 <- targets::tar_read("df_all_data_formatted")|> 
  dplyr::filter(!is.na(pase_0),!is.na(pase_4)) |> 
  # pase_cutter(drop.nas = TRUE) |>
  events_ready(v.groups=c("clin","lifestyle.events","ses", "assess.events","quartiles"))  |>
  dplyr::select(dplyr::all_of(c("pase_4", "age", "reg_female", "nihss_0", "reg_trombolyse", 
"reg_trombektomi", "rtreat_placebo", "reg_alone", "reg_smoker", 
"reg_more_alc", "reg_hyperten", "reg_diabetes", "reg_atriefli", 
"reg_ami", "soc_status_nowork", "fam_indk_hl", "edu_level_hl", 
"who_4", "mdi_4", "mfi_gen_4", "mrs_4_above1", "time", "status"
))) |>
  (\(data){
    # c("pase_0","pase_4") |> 
    #   purrr::map(\(exp){
        list("Univariable"=cox_regression(data=data,all.vars = FALSE, use.strata = FALSE, outcome.var = "pase_4"),
             "Multivariable"=cox_regression(data=data,all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_4")) |> 
          purrr::map(\(.x){
            .x |> gtsummary::tbl_regression(exponentiate=TRUE) |> gtsummary::add_nevent()|> 
  fix_labels()
          }) |> 
          tbl_merged_named()
      # })
    })()   |>
      gtsummary::modify_table_body( ~ filter(.x, variable == "pase_4"))
list("PASE 0"=tbl_cont_0,
     "PASE 6"=tbl_cont_4) |> tbl_stack_named()



# Code from: events_type_sens.qmd
targets::tar_config_set(store = here::here("_targets"))
source(here::here("R/functions.R"))
source(here::here("R/glmnet-reg.R"))
library(targets)
library(tidyverse)
df <- targets::tar_read(df_all_data_formatted) |> 
  dplyr::mutate(
  status.all=dplyr::if_else(
    startsWith(as.character(event),"death"),"death","cve",missing = NA_character_)|> factor()
  )|>
  get_vars(vars.groups = c("clin","lifestyle.events","ses", "ssri")) |> 
  dplyr::rename(status=status.all,
                time=time.all)
df$status |> summary()
tbl <- df |>
  pase_cutter(drop.nas = TRUE, drop.pase = TRUE) |>
  (\(.x) {
    list(
      "death" = .x |> dplyr::mutate(status = dplyr::if_else(status == "death", 1, 0, 0)),
      "cve" = .x |> dplyr::mutate(status = dplyr::if_else(status == "cve", 1, 0, 0))
    )
  })() |>  lapply(\(.x) {
    ls <- list(
      "Univariate" = .x |> dplyr::select(-rtreat) |>
        standard_multi_cox_table(all.vars = FALSE) |> gtsummary::add_nevent(),
      "Multivariate" = .x |> dplyr::select(-rtreat) |>
        standard_multi_cox_table(all.vars = TRUE) |> gtsummary::add_nevent()
    ) |>
      purrr::map(gtsummary::bold_p) |>
      
      tbl_merged_named()
    ls |>
      gtsummary::modify_table_body( ~ filter(.x, variable == "pase_change"))
  })   |>
  gtsummary::tbl_stack(group_header = c("death","cve"))# |>
  # gtsummary::as_gt() |>
  # gt::gtsave(here::here("out/trial_strat_sens.docx"))
     
tbl

names(tbl)



# Code from: mrs_sensitivity.qmd
source(here::here("R/functions.R"))
ls_mrs0 <- targets::tar_read(df_all_data_formatted) |> 
  dplyr::filter(mrs_0==0)|>
    dplyr::filter(!is.na(pase_0),!is.na(pase_4)) |> 
  events_ready() |> 
  (\(.x){
    list(
      std=.x,
      imp=.x |> events_dataset(impute = TRUE)
    )
  })() |> 
  purrr::map(\(.x){
    .x |>
      pase_cutter(drop.pase = TRUE,drop.nas = TRUE)
  }) 

# targets::tar_read(df_all_data_formatted) |> 
#   dplyr::filter(mrs_0==0) |>
#     dplyr::filter(!is.na(pase_0),!is.na(pase_4)) |> events_ready() |>
#   pase_cutter(drop.pase = TRUE,drop.nas = TRUE) |>
#     cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") |>
#     tbl_regression_standard()

ls <- list(
  "Univariable"= ls_mrs0$std |>
    dplyr::select(pase_change,dplyr::everything()) |> 
    splitdf4uvcox(include=c("time","status")) |> 
    purrr::map(\(.x){
      .x |> tbl_regression_standard()
    }) |> 
    gtsummary::tbl_stack(),
  "Multivariable" = ls_mrs0$std |>
    cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") |>
    tbl_regression_standard() |> gtsummary::add_glance_source_note(),
  "Multivariable Imputed"= ls_mrs0$imp |>
    cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") |>
    tbl_regression_standard()
) |> purrr::map(\(.x){
  .x|> 
    gtsummary::modify_table_styling(column = p.value,
                                    hide=TRUE)
}) |> tbl_merged_named()

ls
# |> gtsummary::as_gt() |>
#   gt::gtsave(filename = here::here(glue::glue("out/sens_subset_mrs0_0.docx")))

# ls_mrs0$std |>
#   cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") |> performance::check_model()
targets::tar_read(df_all_data_formatted)|>
    dplyr::filter(!is.na(pase_0),!is.na(pase_4)) |> 
  dplyr::filter(mrs_0==0) |>
  dplyr::select(pase_0,pase_4) |> 
  summary()
# ls_mrs0$std |> gtsummary::tbl_summary(by = pase_change) |> fix_labels()
ls_mrs0_change <- targets::tar_read(df_all_data_formatted) |>
  get_vars(vars.groups =c("clin","lifestyle.events","ses", "assess.events.pre")) |>
  dplyr::filter(event.include) |>
  dplyr::select(-tidyselect::all_of("event.include")) |> 
  (\(.x){
    list(
      std=.x,
      imp=.x |>
      dplyr::select(-tidyselect::any_of("reg_bmi"))|>
      fun_impute(ignore = c("pase_0","pase_4"),pase.mod = FALSE) 
    )
  })() |> 
  purrr::map(\(.x){
    .x |>
      pase_cutter(drop.pase = TRUE,drop.nas = TRUE)
  }) 

# targets::tar_read(df_all_data_formatted) |> 
#   dplyr::filter(mrs_0==0) |>
#     dplyr::filter(!is.na(pase_0),!is.na(pase_4)) |> events_ready() |>
#   pase_cutter(drop.pase = TRUE,drop.nas = TRUE) |>
#     cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") |>
#     tbl_regression_standard()

ls_change <- list(
  "Univariable"= ls_mrs0_change$std |>
    dplyr::select(pase_change,dplyr::everything()) |> 
    splitdf4uvcox(include=c("time","status")) |> 
    purrr::map(\(.x){
      .x |> tbl_regression_standard()
    }) |> 
    gtsummary::tbl_stack(),
  "Multivariable" = ls_mrs0_change$std |>
    cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") |>
    tbl_regression_standard() |> gtsummary::add_glance_source_note(),
  "Multivariable Imputed"= ls_mrs0_change$imp |>
    cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") |>
    tbl_regression_standard()
) |> purrr::map(\(.x){
  .x|> 
    gtsummary::modify_table_styling(column = p.value,
                                    hide=TRUE)
}) |> tbl_merged_named()

ls_change
#| eval: false
coll <- list(
  mrs0_0=ls_mrs0$std|>
    cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change"),
  pase_change_mrs0 = ls_mrs0_change$std|>
  cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change"),
  pase_change=df |>
    get_vars(vars.groups =c("clin","lifestyle.events","ses", "assess.events")) |>
    dplyr::filter(event.include) |>
    dplyr::select(-tidyselect::all_of("event.include")) |> 
    pase_cutter(drop.pase = TRUE,drop.nas = TRUE)|>
    cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change"),
  prestroke_pase=df |>
    pase_cutter(drop.nas = FALSE) |>
    get_vars(vars.groups = c("clin", "lifestyle.events", "ses", "assess.pred", "quartiles"), vars.vec = c("inc_time",
                                                                                                          "time",
                                                                                                          "status")) |>
    dplyr::mutate(time = time + inc_time / 365) |>
    dplyr::select(-dplyr::any_of(c("pase_0", "pase_4", "pase_change", "inc_time", "event.include", "pase_4_quartile")))|>
    cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_0_quartile")) |> 
    purrr::map(performance::check_collinearity)

coll |> purrr::imap(\(.x,.i){
  .x |> dplyr::as_tibble() |>  gt::gt() |> gt::tab_header(.i)|> 
  gt::gtsave(filename = here::here(glue::glue("out/coll_{.i}.docx")))
})



# Code from: pa_event_plots.qmd
targets::tar_config_set(store = here::here("_targets"))
source(here::here("R/functions.R"))
source(here::here("R/glmnet-reg.R"))
library(targets)
library(tidyverse)
# targets::tar_read(plot_events_survival_smooth)
p1 <- targets::tar_read(df_event_data) |>
  events_dataset(impute = FALSE) |>
  dplyr::mutate(pase_change = factor(pase_change, 
                                     levels = c("Persistently high", "Decrease", "Increase", "Persistently low")),
                pase_change = dplyr::recode(pase_change,
                                            "Persistently high"="Consistently above lowest", 
                                            "Decrease"="Decrease to lowest", 
                                            "Increase"="Increase from lowest", 
                                            "Persistently low"="Consistently lowest")
                ) |>
  cox_regression() |>
  plot_survival_smooth(line.w = 1) +
  ggplot2::labs(
    fill = "PA level change group",
    color = "PA level change group",
    linetype = "PA level change group"
  ) +
  ggplot2::scale_x_continuous(limits = c(0, 9.5), breaks = c(0, 3, 6, 9))


ggplot2::ggsave(here::here("out/smooth_surv.png"), 
                p1, 
                dpi = 600, 
                units = "cm", 
                height = 8, 
                width = 15)
p1
surv.data <- targets::tar_read(df_event_data) |>
  events_dataset(impute = FALSE) |>
  cox_regression(all.vars = FALSE) |> # Minimal model to just give risk table
  ggsurvfit::survfit2() |>
  ggsurvfit::tidy_survfit(times = c(0, 3, 6, 9))


risk_event <- c("n.risk", "cum.event") |>
  purrr::map2(c("Numbers at risk", "Events"), \(.x, .y){
    surv.data |>
      tidyr::pivot_wider(id_cols = strata, names_from = time, values_from = {{ .x }}) |>
      mask_micro_table(col.sel = -strata) |> # Masking columns
      tidyr::pivot_longer(cols = -strata) |>
      setNames(c("strata", "time", .x)) |>
      dplyr::mutate(dplyr::across(time, ~ as.numeric(.x))) |>
      dplyr::mutate(strata = factor(strata, levels = rev(c("Persistently high", "Decrease", "Increase", "Persistently low")))) |>
      ggplot2::ggplot(ggplot2::aes(x = time, y = strata, label = get(.x))) +
      ggplot2::geom_text() +
      ggplot2::labs(y = NULL, title = .y) +
      ggplot2::theme_minimal() +
      ggsurvfit::theme_risktable_default()
  })

p2 <- ggsurvfit::ggsurvfit_align_plots(list(p1, risk_event[1]) |> purrr::list_flatten()) |>
  patchwork::wrap_plots(ncol = 1, heights = c(2, 1), guides = "collect")

ggplot2::ggsave(here::here("out/smooth_surv_tables.png"), 
                p2, 
                dpi = 600, 
                units = "cm", 
                height = 9, 
                width = 15)
p2
#| include: false
targets::tar_read(df_event_data) |>
  events_dataset(impute = FALSE) |>
  dplyr::mutate(pase_change = factor(pase_change, levels = c("Persistently high", "Decrease", "Increase", "Persistently low"))) |>
  cox_regression(all.vars = FALSE) |> # Minimal model to just give risk table
  ggsurvfit::survfit2() |>
  ggsurvfit::ggsurvfit() +
  ggplot2::scale_y_continuous(
    limits = c(0, 1.02),
    breaks = seq(0, 1, .25),
    labels = scales::percent,
    expand = c(0.01, 0)
  ) +
  ggplot2::scale_x_continuous(breaks = c(0, 4, 8.5), expand = c(0.02, 0)) +
  ggsurvfit::add_risktable()
#| include: false
targets::tar_read(df_event_data_small) |>
  events_dataset(impute = FALSE) |>
  dplyr::mutate(pase_change = factor(pase_change, levels = c("Persistently high", "Decrease", "Increase", "Persistently low"))) |>
  cox_regression() |>
  plot_survival_smooth() +
  ggplot2::labs(
    fill = "PA change group",
    color = "PA change group",
    linetype = "PA change group"
  )
#| include: false
targets::tar_read(df_event_data) |>
  events_dataset(impute = FALSE) |>
  dplyr::mutate(pase_change = factor(pase_change, levels = c("Persistently high", "Decrease", "Increase", "Persistently low"))) |>
  cox_regression() |>
  plot_survival_smooth() +
  ggplot2::labs(
    fill = "PA change group",
    color = "PA change group",
    linetype = "PA change group"
  )



# Code from: pa_events_analyses.qmd
targets::tar_config_set(store = here::here("_targets"))
source(here::here("R/functions.R"))
source(here::here("R/glmnet-reg.R"))
library(targets)
library(tidyverse)
#| include: false
list("Univariate"=targets::tar_read(tbl_events_cox_regression_uv),
"Multivariate"=targets::tar_read(tbl_events_cox_regression),
"Imputed Multivariate"=targets::tar_read(tbl_events_mids_cox_regression)) |> 
  # purrr::map(gtsummary::modify_table_styling,column=p.value,hide=TRUE) |> 
  purrr::map(gtsummary::bold_p) |> 
  tbl_merged_named()
#| include: true
list("Univariate"=targets::tar_read(tbl_events_cox_regression_uv),
"Multivariate"=targets::tar_read(tbl_events_cox_regression),
"Imputed Multivariate"=targets::tar_read(tbl_events_mids_cox_regression)) |> 
  purrr::map(gtsummary::modify_table_styling,column=p.value,hide=TRUE) |> 
  tbl_merged_named()
#| echo: true
targets::tar_read(df_event_data) |> 
  # dplyr::select(-reg_bmi) |>
  na.omit() |> 
  nrow()
#| include: false
targets::tar_read(tbl_events_cox_regression_small)

targets::tar_read(df_event_data_small)
#| include: false
targets::tar_read(tbl_events_mids_cox_regression)
targets::tar_read("df_all_data_formatted") |> 
  events_ready() |> 
  dplyr::filter(!is.na(pase_0),!is.na(pase_4)) |> 
  (\(data){
    c("pase_0","pase_4") |> 
      purrr::map(\(exp){
        with(data,survival::coxph(as.formula(glue::glue("survival::Surv(time, status)~{exp}")))) |> 
  gtsummary::tbl_regression(exponentiate=TRUE)|> 
  fix_labels()
      })
    })()  |> 
  gtsummary::tbl_stack()
targets::tar_read("df_all_data_formatted") |> 
  pase_cutter(drop.nas = TRUE) |> 
  events_ready(v.groups=c("clin","lifestyle.events","ses", "assess.events","quartiles"))  |> 
  dplyr::select(-dplyr::any_of(c("pase_0","pase_4","pase_change"))) |> 
  (\(data){
    c("pase_0_quartile","pase_4_quartile") |> 
      purrr::map(\(exp){
        list("Univariable"=cox_regression(data=data,all.vars = FALSE, use.strata = FALSE, outcome.var = exp),
             "Multivariable"=cox_regression(data=data,all.vars = TRUE, use.strata = FALSE, outcome.var = exp)) |> 
          purrr::map(\(.x){
            .x |> gtsummary::tbl_regression(exponentiate=TRUE)|> 
  fix_labels()
          }) |> 
          tbl_merged_named()
      })
    })()  |> 
  gtsummary::tbl_stack()
targets::tar_read("df_all_data_formatted") |> 
  pase_cutter(drop.nas = TRUE) |>
    get_vars(vars.groups = c("clin","lifestyle.events","ses", "assess.events","quartiles"),vars.vec = c("inc_time"))  |> 
  dplyr::mutate(time=time+inc_time/365) |> 
  dplyr::select(-dplyr::any_of(c("pase_0","pase_4","pase_change","inc_time","event.include","pase_4_quartile"))) |> 
  (\(data){
    list(
        with(data,survival::coxph(as.formula(glue::glue("survival::Surv(time, status)~{exp}")))) |> 
  gtsummary::tbl_regression(exponentiate=TRUE)|> 
  fix_labels() |> gtsummary::add_glance_source_note()
      )
    })()  |> 
  gtsummary::tbl_stack()



# Code from: pa_events_summaries.qmd
targets::tar_config_set(store = here::here("_targets"))
source(here::here("R/functions.R"))
source(here::here("R/glmnet-reg.R"))
library(targets)
library(tidyverse)
#| include: false
targets::tar_read(tbl_events_summary)
#| include: true
targets::tar_read(tbl_events_summary) |> mask_micro_summary(micro.n = 5)
#| include: false
ls <- targets::tar_read(df_event_data)|>
    pase_cutter(drop.pase = TRUE) |>
    dplyr::select(-tidyselect::all_of(c("status", "time"))) |>
    dplyr::filter(!is.na(pase_change)) |> 
    labelling_data() |>
    gtsummary::tbl_summary(
      missing = "ifany",
      by = pase_change,
      # value = list(where(is.logical) ~ TRUE),
      missing_text = "Missing"
    ) |>
    gtsummary::add_overall() |>
    gtsummary::add_n()

ls |> add_missing_stats() |>  mask_micro_summary(micro.n = 5)
targets::tar_read(df_event_data)|>
    pase_cutter(drop.pase = TRUE) |>
    dplyr::select(pase_change, time) |>
    dplyr::filter(!is.na(pase_change)) |> 
    labelling_data() |>
    gtsummary::tbl_summary(
      missing = "ifany",
      by = pase_change,
      type = gtsummary::all_continuous() ~ "continuous2",
      statistic = list(gtsummary::all_continuous() ~ c("{median} ({p25}, {p75})","{mean} ({sd})")),
      # value = list(where(is.logical) ~ TRUE),
      
      missing_text = "Missing"
    ) |>
    gtsummary::add_overall()
#| include: true
targets::tar_read(df_all_data_formatted) |>
  (\(.x)summary(.x$pase_0))()

# targets::tar_read(df_all_data_formatted) |>
#    dplyr::filter(!is.na(pase_0),!is.na(pase_4))|>
# (\(.x)summary(.x$pase_0))()
#| include: false
targets::tar_read(df_all_data_formatted) |>
  pase_cutter(drop.pase = TRUE) |>
  dplyr::select(mrs_4, pase_change) |>
  dplyr::mutate(pase_change = forcats::fct_relevel(pase_change, c("Persistent high", "Increase", "Decrease", "Persistent low"))) |>
  na.omit() |>
  (\(.x){
    table(mrs = .x$mrs_4, pase = .x$pase_change)
  })() |>
  rankinPlot::grottaBar(groupName = "pase", scoreName = "mrs")
targets::tar_read(df_event_data)|>
    pase_cutter(drop.pase = FALSE) |>
    dplyr::filter(!is.na(pase_change))|>
    dplyr::select(pase_0,pase_4)  |> 
    labelling_data() |> 
  gtsummary::tbl_summary()
targets::tar_read(df_all_data_formatted)|>
    pase_cutter(drop.pase = FALSE) |>
    dplyr::filter(!is.na(pase_change))|>
    dplyr::count(pase_0_quartile,pase_4_quartile) |> 
  write_csv(here::here("out/event_sankey_data.csv"))
ds <- targets::tar_read(df_all_data_formatted) |>
  get_vars(vars.groups = c("clin", "lifestyle", "ses", "assess.events", "extra")) |>
  dplyr::select(-time, -status, -soc_status) |>
  pase_cutter(drop.pase = TRUE) |>
  labelling_data()

ls <- list(
  pase_out = ds |>
    dplyr::mutate(event.filter = factor(
      dplyr::case_when(is.na(pase_change) ~ "pase_incomplete",
        !event.include ~ "early_event",
        .default = "included"
      ),
      levels = c("pase_incomplete", "early_event", "included")
    )),
  event_out = ds |>
    dplyr::mutate(event.filter = factor(
      dplyr::case_when(!event.include ~ "early_event",
        is.na(pase_change) ~ "pase_incomplete",
        .default = "included"
      ),
      levels = c("early_event", "pase_incomplete", "included")
    ))
) |>
  purrr::map(dplyr::select, -event.include, -pase_change, -event)

ls_tbl <- ls |>
  purrr::map(\(.x){
    .x |>
      dplyr::filter(event.filter != "pase_incomplete") |>
      dplyr::mutate(event.filter = factor(event.filter))
  }) |>
  (\(.x) list(.x, purrr::pluck(ls, 1) |> dplyr::mutate(event.filter = event.filter == "included")))() |>
  purrr::list_flatten()
#| include: false
ls_tbl |>
  purrr::map(\(.x){
    .x |>
      gtsummary::tbl_summary(
        by = event.filter,
        missing = "ifany"
      ) |>
      gtsummary::add_p()
  }) |>
  (\(.x)gtsummary::tbl_merge(.x, c(names(.x)[1:2], "all_out")))()
targets::tar_read(df_all_data_formatted) |>
  get_vars(vars.groups = c("clin", "lifestyle", "ses", "assess.events", "extra", "assess.pred")) |>
  dplyr::select(-time, 
                # -status, 
                -soc_status) |>
  pase_cutter(drop.pase = FALSE) |>
  labelling_data() |>
    dplyr::mutate(event.filter = factor(
      dplyr::case_when(
        !event.include | is.na(pase_change) ~ "excluded",
        .default = "included"
      )
    )) |>
  dplyr::select(-who_4, -mdi_4, -mrs_4_above1, -mfi_gen_4) |>
      gtsummary::tbl_summary(
        by = event.filter,
        missing = "ifany"
      ) |>
      gtsummary::add_p()
ds |>
  dplyr::mutate(pase_incomplete=is.na(pase_change),
                excluded=pase_incomplete | !event.include,
                early_event=!event.include) |> 
  dplyr::select(early_event,pase_incomplete,excluded) |> 
  gtsummary::tbl_summary(by=excluded)
#| include: true
ls_tbl |>
  purrr::pluck(3) |>
  dplyr::select(-who_4, -mdi_4, -mrs_4_above1, -mfi_gen_4) |>
  (\(.x){
    .x |>
      gtsummary::tbl_summary(
        by = event.filter,
        missing = "no"
      ) |>
      gtsummary::add_p()
  })() |> mask_micro_summary(micro.n = 5)
#| include: true
targets::tar_read(df_event_data)|>
    pase_cutter(drop.pase = FALSE)|>
    dplyr::filter(!is.na(pase_change)) |>
    dplyr::select(-time, -status, -pase_0_quartile, -pase_4_quartile, -pase_change)  |> 
    labelling_data() |> 
  dplyr::mutate(reg_female=ifelse(reg_female,"Female","Male")) |>
    gtsummary::tbl_summary(
      missing = "ifany",
      by = reg_female,
      value = list(where(is.logical) ~ TRUE)
    )|> gtsummary::add_p() |> 
  mask_micro_summary()
#| include: false
events <- targets::tar_read(ls_all_events) |>
  dplyr::bind_rows() |>
  dplyr::left_join(targets::tar_read(df_all_data_formatted) |>
    dplyr::select(c("event.include", "rdate", "enddate", "PNR")), by = c("CPR" = "PNR")) |>
  dplyr::mutate(
    date.event = as.Date(date.event),
    event.trial = !date.event > enddate,
    event.itt = !date.event > (lubridate::dmonths(6) + rdate)
  ) |>
  dplyr::mutate(event.type = dplyr::if_else(grepl("^death", event.type), "death", event.type))

ls <- list(
  # Events during inclusion and during first 6 months after inclusion (Intention to treat)
  events |>
    dplyr::select(event.type, event.trial, event.itt) |>
    tidyr::pivot_longer(cols = c("event.trial", "event.itt")) |>
    dplyr::filter(value) |>
    dplyr::select(-value) |>
    gtsummary::tbl_summary(by = name),

  # All registred events
  events |>
    dplyr::select(event.type) |>
    gtsummary::tbl_summary(),

  # All events in the selected group
  events |>
    dplyr::select(event.type, event.include) |>
    # tidyr::pivot_longer(cols = c("event.trial","event.itt")) |>
    dplyr::filter(event.include) |>
    dplyr::select(-event.include) |>
    gtsummary::tbl_summary(),

  # All considered events (first event)
  targets::tar_read(df_events_deaths)|>
    dplyr::mutate(event.type = dplyr::if_else(grepl("^death", event.type), "death", event.type)) |>
    dplyr::select(event.type) |>
    gtsummary::tbl_summary(),
  
   # All included events (first event)
  targets::tar_read(df_all_data_formatted)|> 
  pase_cutter(drop.pase = TRUE) |>
  dplyr::filter(!is.na(pase_change))|>
    dplyr::filter(event.include) |>
    # dplyr::select(-event.include)|>
    dplyr::mutate(event = dplyr::if_else(grepl("^death", event), "death", event)) |>
    dplyr::select(event) |>
    dplyr::filter(!is.na(event)) |> 
    gtsummary::tbl_summary()
) |>
  setNames(c("During trial", "All events", "Selected events", "Considered", "Included"))

ls[4] |> 
  purrr::map(\(.x) .x |>
               mask_micro_summary(micro.n = 5)) |>
  tbl_merged_named()
targets::tar_read(df_all_data_formatted)|> 
  pase_cutter(drop.pase = TRUE) |>
  dplyr::filter(!is.na(pase_change))|>
    dplyr::filter(event.include) |>
    # dplyr::select(-event.include)|>
    dplyr::mutate(event = dplyr::case_when(grepl("^death", event)~"Mors",
                                           grepl("^DI6", event)~ "DI61-4",
                                           .default = event)) |>
    dplyr::select(event) |>
    dplyr::filter(!is.na(event)) |> 
    gtsummary::tbl_summary() |> mask_micro_summary(micro.n = 5)
targets::tar_read(df_event_data) |>
  pase_cutter(drop.pase = TRUE) |>
  dplyr::filter(!is.na(pase_change)) |> 
  events_table(by="pase_change")



# Code from: sex_events.qmd
targets::tar_config_set(store = here::here("_targets"))
source(here::here("R/functions.R"))
source(here::here("R/glmnet-reg.R"))
library(targets)
library(tidyverse)

df <- targets::tar_read(df_all_data_formatted) |> 
  get_vars(vars.groups = c("clin","lifestyle.events","ses", "ssri")) |> 
  dplyr::rename(status=status.all,
                time=time.all)
df |>
  pase_cutter(drop.nas = TRUE,drop.pase = TRUE) |>
  dplyr::mutate(reg_female=factor(ifelse(reg_female,"Female","Male"))) |> 
  (\(.x) {
    split(.x, .x$reg_female)
  })() |> lapply(\(.x) {
    ls <- list("Univariate"=.x |> dplyr::select(-reg_female,-rtreat) |>
      standard_multi_cox_table(all.vars = FALSE) |> gtsummary::add_nevent(),
"Multivariate"=.x |> dplyr::select(-reg_female,-rtreat) |>
      standard_multi_cox_table(all.vars = TRUE) |> gtsummary::add_nevent())|> 
  purrr::map(gtsummary::bold_p) |> 
  tbl_merged_named() 
    ls |> 
       gtsummary::modify_table_body(~filter(.x, variable == "pase_change"))
  })   |> 
  (\(.x) {
    gtsummary::tbl_stack(.x,group_header=names(.x)) 
  })()
  





# Code from: ssri_events.qmd
targets::tar_config_set(store = here::here("_targets"))
source(here::here("R/functions.R"))
source(here::here("R/glmnet-reg.R"))
library(targets)
library(tidyverse)

df <- targets::tar_read(df_all_data_formatted) |> 
  get_vars(vars.groups = c("clin","lifestyle.events","ses", "ssri")) |> 
  dplyr::rename(status=status.all,
                time=time.all)
df |> 
  dplyr::select(#-event.include,
                -rtreat_placebo)|>
  labelling_data() |> 
  gtsummary::tbl_summary(by=rtreat) |> 
  gtsummary::add_overall() #|> 
  # mask_micro_summary()
df |> 
  dplyr::select(
    -rtreat_placebo,
                -pase_4#,
    # -reg_bmi
                )  |> 
  cox_regression(outcome.var = "rtreat",use.strata = FALSE) |> 
  gtsummary::tbl_regression(exponentiate = TRUE, 
                                    add_estimate_to_reference_rows = TRUE,
                                    show_single_row = where(is.logical)) |> 
  gtsummary::bold_p() |> 
  fix_labels()
df |> 
  dplyr::select(-rtreat_placebo) |> 
  cox_regression(outcome.var = "rtreat",all.vars = FALSE, use.strata = TRUE,include_formula = TRUE) |> 
  plot_survival_smooth()
df |>
  pase_cutter(drop.nas = TRUE,drop.pase = TRUE) |>
  (\(.x) {
    split(.x, .x$rtreat)
  })() |> lapply(\(.x) {
    ls <- list("Univariate"=.x |> dplyr::select(-rtreat_placebo, -rtreat) |>
      standard_multi_cox_table(all.vars = FALSE),
"Multivariate"=.x |> dplyr::select(-rtreat_placebo, -rtreat) |>
      standard_multi_cox_table(all.vars = TRUE))|> 
  purrr::map(gtsummary::bold_p) |> 
  tbl_merged_named() 
    ls |> 
       gtsummary::modify_table_body(~filter(.x, variable == "pase_change"))
  })   |> tbl_stack_named()
  # gtsummary::tbl_stack(group_header=levels(factor(df$rtreat)))