absolute difference in the probability of an event using Kaplan-Meier plots in ggsurvfit

Question

I would like to calculate the absolute difference in the probability of an event occurring within a specified duration of follow-up from a Kaplan-Meier plot. How can I achieve this using ggsurvfit?" Below is an example code:

library(ggsurvfit)
library(tidyverse)

survfit2(Surv(time, status) ~ sex, data = df_lung) %>%
  ggsurvfit()+
  add_confidence_interval() +
  add_risktable() 

Answer 1

Not sure how exactly you want the difference displayed, but here's an example of calculating the difference and adding it to the plot region.

library(ggsurvfit)
#> Loading required package: ggplot2

sf <- survfit2(Surv(time, status) ~ sex, data = df_lung) 

df_difference <-
  sf |> 
  tidy_survfit(times = 12) |> 
  dplyr::select(strata, time, estimate) |> 
  tidyr::pivot_wider(
    id_cols = time, 
    values_from = estimate,
    names_from = strata
  ) |> 
  dplyr::mutate(
    survival_difference = 
      scales::label_percent(accuracy = 0.01)(Female - Male)  
  )
df_difference
#> # A tibble: 1 × 4
#>    time Female  Male survival_difference
#>   <dbl>  <dbl> <dbl> <chr>              
#> 1    12  0.526 0.336 19.04%

sf |> 
  ggsurvfit() +
  add_confidence_interval() +
  add_risktable() +
  add_quantile(x = 12, color = "grey") +
  geom_text(
    data = df_difference, 
    aes(x = 0, y = 0.45, label = paste("Difference:", survival_difference)),
    vjust = "inward",
    size = 3,
    angle = 90
  ) +
  scale_ggsurvfit(x_scales = list(breaks = seq(0, 30, by = 6)))

^{Created on 2024-02-08 with reprex v2.1.0}