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Stratified Survival Analysis

Introduction

singleEventSurvival() supports two stratifiers:

If both are requested, the package fits each stratifier separately. It does not build joint strata such as Female, 65+.

Example Data

library(OdysseusSurvivalModule)

survivalData <- data.frame(
  subject_id = 1:12,
  time = c(20, 35, 42, 50, 63, 70, 74, 85, 91, 105, 118, 140),
  status = c(1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0),
  age_years = c(34, 37, 41, 48, 52, 58, 61, 66, 71, 73, 77, 82),
  gender = c("Female", "Male", "Female", "Male", "Female", "Male", "Female", "Male", "Female", "Male", "Female", "Male")
)

Gender Stratification

fitGender <- singleEventSurvival(
  survivalData = survivalData,
  timeScale = "days",
  model = "km",
  strata = "gender"
)

names(fitGender)
fitGender[["overall"]]$summary
fitGender[["gender=Female"]]$summary
fitGender[["gender=Male"]]$summary
fitGender$logrank_test_gender

The log-rank output is stored in logrank_test_gender.

Age-Group Stratification

fitAge <- singleEventSurvival(
  survivalData = survivalData,
  timeScale = "days",
  model = "km",
  strata = "age_group",
  ageBreaks = list(c(18, 49), c(50, 64), c(65, Inf))
)

names(fitAge)
fitAge[["age_group=18-49"]]$summary
fitAge[["age_group=50-64"]]$summary
fitAge[["age_group=65+"]]$summary
fitAge$logrank_test_age_group

Age-group labels are generated automatically from ageBreaks.

Using Both Stratifiers

fitBoth <- singleEventSurvival(
  survivalData = survivalData,
  timeScale = "days",
  model = "km",
  strata = c("gender", "age_group"),
  ageBreaks = list(c(18, 49), c(50, 64), c(65, Inf))
)

names(fitBoth)
fitBoth$logrank_test_gender
fitBoth$logrank_test_age_group

In this case the result includes:

Extract Stratum-Specific Curves

femaleCurve <- fitGender[["gender=Female"]]$data
olderCurve <- fitAge[["age_group=65+"]]$data

head(femaleCurve)
head(olderCurve)

Plot Separate Strata

plot(
  fitGender[["gender=Female"]]$data$time,
  fitGender[["gender=Female"]]$data$survival,
  type = "s",
  col = "firebrick",
  xlab = "Time (days)",
  ylab = "Survival probability",
  ylim = c(0, 1),
  main = "Gender-specific Kaplan-Meier curves"
)

lines(
  fitGender[["gender=Male"]]$data$time,
  fitGender[["gender=Male"]]$data$survival,
  type = "s",
  col = "steelblue"
)

legend(
  "topright",
  legend = c("Female", "Male"),
  col = c("firebrick", "steelblue"),
  lty = 1,
  bty = "n"
)

Stratified Cox and Parametric Fits

The same stratification behavior applies to other models.

fitCox <- singleEventSurvival(
  survivalData = survivalData,
  timeScale = "days",
  model = "cox",
  covariates = c("age_years"),
  strata = "gender"
)

fitWeibull <- singleEventSurvival(
  survivalData = survivalData,
  timeScale = "days",
  model = "weibull",
  covariates = c("age_years"),
  strata = "age_group",
  ageBreaks = list(c(18, 49), c(50, 64), c(65, Inf))
)

fitCox[["gender=Female"]]$summary
fitWeibull[["age_group=65+"]]$summary

Summary

For stratified analysis, the main points are:

  1. Use strata = "gender", strata = "age_group", or both.
  2. Read stratum-specific results from named list entries.
  3. Use logrank_test_gender and logrank_test_age_group for between-group tests.
  4. Treat gender and age-group results as separate stratified analyses, not joint cells.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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