Landmarked example

This package provides a function ggsurvplotlm to create landmark survival plots using the ggsurvplot function from the survminer package. The function takes a survfit object, a landmark time, and an optional label for the landmark time as inputs and returns a modified ggsurvplot object.

library(landmarked)
library(survminer)
#> Loading required package: ggplot2
#> Warning: package 'ggplot2' was built under R version 4.5.3
#> Loading required package: ggpubr
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

Using the lung dataset from the survival package, we visualise the survival curves for male and female patients. This example is one in which a landmark analysis is not necessary. Sex is known at time 0 and hence it is reasonable to create separate curves by this variable from this time.

A landmark analysis addresses the problem of survivor bias in scenarios where the variable of interest is not known at baseline, for example, treatment response. In such cases, splitting curves from time=0 as though group membership were known in advance introduces bias. Splitting curves according to status at a specified landmark time point provides an unbiased comparison.

After converting days to months, we can plot the Kaplan-Meier estimates using ggsurvplot with our desired formatting.

data = survival::lung |> 
  mutate(months = time/30.4375) |> 
  select(months,status, sex)

fit = survival::survfit(survival::Surv(months, status) ~ sex, data)

ggsurvplot(
  fit,    
  data = data, 
  ylab="survival (%)",
  xlab="Time (months)",
  break.time.by = 6,
  conf.int = FALSE,
  legend = "none",
  surv.scale = "percent",
  xlim= c(0,25),
  surv.median.line = "hv",
  palette = c("#2E9FDF","#E7B800"),
  #Standard configuration
  risk.table = c("nrisk_cumcensor"),
  ggtheme = theme_survminer()+theme(),
  tables.theme = theme_void() + theme(plot.title = element_text(size =10)),
  risk.table.y.text.col = T, 
  risk.table.y.text = FALSE, 
  axes.offset=FALSE,
  risk.table.fontsize =3,
  tables.height=0.2
)
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.
#> ℹ The deprecated feature was likely used in the ggpubr package.
#>   Please report the issue at <https://github.com/kassambara/ggpubr/issues>.
#> This warning is displayed once per session.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> Ignoring unknown labels:
#> • colour : "Strata"

The ggsurvplotlm function can be used to create a landmark survival plot. The pooled Kaplan-Meier estimate is calculated prior to the landmark time and used to adjust the remaining estimates so that survival probabilities beyond the landmark can be interpreted relative to the baseline time point.

In this example, we set the landmark time to 6 months and label it “Landmark”. The function adjusts the Kaplan-Meier estimates accordingly. As with ggsurvplot, you can pass additional plotting parameters to ggsurvplotlm, allowing the plot to be formatted as needed.

ggsurvplotlm(
  fit,                     
  landmark_time = 6,
  landmark_label = "Landmark",
  data = data,  
  ylab="survival (%)",
  xlab="Time (months)",
  break.time.by = 6,
  conf.int = FALSE,
  legend = "none",
  surv.scale = "percent",
  xlim= c(0,25),
  surv.median.line = "hv",
  palette = c("#2E9FDF","#E7B800"),
  ggtheme = theme_survminer()+theme(),
  axes.offset=FALSE
)