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

TL;DR

This paper provides a finite population inference perspective for the logrank test in censored outcomes, demonstrating its asymptotic validity under randomization without distributional assumptions.

Contribution

It introduces a finite population inference framework for the logrank test, extending its justification beyond classical superpopulation assumptions, especially under complex censoring mechanisms.

Findings

Logrank test is asymptotically valid under Bernoulli randomized experiments.

Validity holds without distributional assumptions on potential event times.

Extension to stratified logrank test for block designs and varying censoring mechanisms.

Abstract

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 non-informative 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. In sum, the developed theory for the logrank test from finite population inference supplements its classical theory from usual superpopulation inference, and helps provide a broader justification for the logrank test.