1 PA Decline/00master.R

@@ -46,7 +46,7 @@ source("data_format.R")
 library(tidyverse)
 library(glue)
 library(patchwork)
-# library(ggdendro)
+library(ggdendro)
 library(corrplot)
 library(gt)
 library(gtsummary)
@@ -106,6 +106,7 @@ ls<-lapply(1:ncol(X_tbl_f),function(x){
 
 ts_e<-tbl_summary(X_tbl,
                   missing = "no",
+                  # label = ls[-length(ls)], ## Removing the last, as this is output
                   value = list(where(is.factor) ~ "2"),
                   type = list(mrs_0 ~ "categorical",
                               mrs_1 ~ "categorical"),
@@ -123,55 +124,6 @@ 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
-
-
-
-## ====================================================================
-# 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))
-
 
 ## ====================================================================
 ## 
@@ -240,7 +192,6 @@ writeLines(com_coef_tbl%>%as_rtf(), com_coef_rtf)
 close(com_coef_rtf)
 
 
-
 ## ====================================================================
 ## 
 ## Model performance

1 PA Decline/data_format.R

@@ -71,33 +71,35 @@ dta <- export %>%
 # 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, 
+dta<-select(dta,c(age,
-                  "mrs_1",
+                  male_sex,
-                  "mfi_gen_1",
+                  civil,
-                  "mfi_phys_1",
+                  pase_0,
-                  "mfi_act_1",
+                  smoker,
-                  "mfi_mot_1",
+                  # smoker_prev,
-                  "mfi_men_1",
+                  alc,
-                  "mdi_1", 
+                  afli,
-                  "who5_score_1"
+                  hypertension,
+                  diabetes,
+                  pad,
+                  ami,
+                  tci,
+                  mrs_0,
+                  nihss_c,
+                  # thrombolysis,
+                  # thrombechtomy,
+                  any_rep,
+                  rtreat,
+                  pase_6, 
+                  mrs_1,
+                  mfi_gen_1,
+                  mfi_phys_1,
+                  mfi_act_1,
+                  mfi_mot_1,
+                  mfi_men_1,
+                  mdi_1, 
+                  who5_score_1
                   ))
 
 ## ====================================================================
@@ -112,10 +114,10 @@ var.labels = c(age="Age",
                smoker="Daily or occasinally smoking",
                # smoker_prev="Previous habbit of smoking",
                alc="More alcohol than recommendation",
-               afli="AFIB",
+               afli="Known AFIB",
-               hypertension="Hypertension",
+               hypertension="Known hypertension",
-               diabetes="Diabetes",
+               diabetes="Known diabetes",
-               pad="PAD",
+               pad="Known PAD",
                ami="Previous MI",
                tci="Previous TIA",
                mrs_0="Pre-stroke mRS  [-1]",
@@ -191,7 +193,7 @@ X_tbl <- X_tbl|>
                                  detail.lst=FALSE),
          pase_drop_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))])
+label(X_tbl) = as.list(var.labels[match(names(X_tbl), names(var.labels))])
 
 # Setting final primary output from "pout"
 if (pout=="decl_rel"){

2 Longterm/grotta_bars.R

@@ -20,7 +20,3 @@ grottaBar(x,groupName="Group",
 dta |> select(-c("mrs_0","mrs_1")) |> generic_stroke(group = "rtreat", score = "mrs_6", variables = c("hypertension","diabetes","civil"))
 
 library(stRoke)
-cc<-dta[complete.cases(dta),]
-talos <- cc[sample(1:nrow(cc),200),] |> select(-mrs_0)
-
-save(talos,file="talos.rda")