# 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()