Randomization-Based Test for Censored Outcomes: A New Look at the Logrank Test

Xinran Li, Dylan S. Small

Research output: Contribution to journalArticlepeer-review


Two-sample tests with censored outcomes are a classical topic in statistics with wide use even in cutting edge applications. There are at least two modes of inference used to justify two-sample tests. One is usual superpopulation inference assuming that units are independent and identically distributed (i.i.d.) samples from some superpopulation; the other is finite population inference that relies on the random assignments of units into different groups. When randomization is actually implemented, the latter has the advantage of avoiding distributional assumptions on the outcomes. In this paper, we focus on finite population inference for censored outcomes, which has been less explored in the literature. Moreover, we allow the censoring time to depend on treatment assignment, under which exact permutation inference is unachievable. We find that, surprisingly, the usual logrank test can also be justified by randomization. Specifically, under a Bernoulli randomized experiment with noninformative i.i.d. censoring, the logrank test is asymptotically valid for testing Fisher’s null hypothesis of no treatment effect on any unit. The asymptotic validity of the logrank test does not require any distributional assumption on the potential event times. We further extend the theory to the stratified logrank test, which is useful for randomized block designs and when censoring mechanisms vary across strata.

Original languageEnglish (US)
Pages (from-to)92-107
Number of pages16
JournalStatistical Science
Issue number1
StatePublished - 2023


  • Potential outcome
  • design-based inference
  • noninformative censoring
  • potential censoring time
  • stratified logrank test

ASJC Scopus subject areas

  • Statistics and Probability
  • General Mathematics
  • Statistics, Probability and Uncertainty


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