update

report.Rmd

@@ -14,6 +14,7 @@ library(survival)
 library(emmeans)
 library(foreign)
 library(gtsummary)
+library(ggsurvfit)
 ```
 
 ```{r}
@@ -114,6 +115,29 @@ table.split <- dat.table1 |>
 table.split
 ```
 
+```{r}
+table.split <- dat.table1 |> 
+  select(sex, age.rec, age.donor, hla.match, cold.isc, tx.type) |> 
+  tbl_summary(
+    by = tx.type,
+    statistic = list(
+      all_continuous() ~ "{median} ({p25}, {p75})"
+    ),
+    label = list(
+      hla.match ~ "HLA matches, n(%)",
+      age.donor ~ "Donor age, median (IQR)",
+      age.rec ~ "Recipient age, median (IQR)",
+      cold.isc ~ "Cold ischemic time (hours), median (IQR), ",
+      sex ~ "Sex, n(%)"
+    ),
+    missing = "ifany"
+  ) |> 
+  add_overall() |> 
+  modify_footnote(all_stat_cols() ~ NA)
+
+table.split
+```
+
 
 Calculate median follow-up (reverse Kaplan-Meier method), with 95% CI
 ```{r}
@@ -367,9 +391,6 @@ recode.dat <- function(dat, time.intervals) {
 
   df
 }
-
-
-
 ```
 
 ```{r}
@@ -547,13 +568,16 @@ dat.age.rec$age.group <- sapply(dat.age.rec$age.rec, class.age) |>
 
 
 cox.age.rec <- coxph(Surv(follow.up, death) ~ age.group, data = dat.age.rec)
-cox.age.rec |> summary()
 ```
 
 ```{r}
 emmeans(cox.age.rec, pairwise ~ age.group, type = "response")
 ```
 
+```{r}
+cox.age.rec |> ggsurvfit()
+```
+
 
 # Exercise 7