Principled Selection of Baseline Covariates to Account for Censoring in Randomized Trials with a Survival Endpoint

TL;DR

This paper proposes a simple, data-driven covariate selection method for survival analysis in randomized trials, aiming to improve validity of treatment effect tests under censoring.

Contribution

It introduces a new covariate selection strategy that ensures valid null hypothesis testing in large samples, addressing issues of model misspecification and censoring assumptions.

Findings

Method works well in simulations

Applicable with standard Cox regression software

Provides valid tests under large-sample conditions

Abstract

The analysis of randomized trials with time-to-event endpoints is nearly always plagued by the problem of censoring. As the censoring mechanism is usually unknown, analyses typically employ the assumption of non-informative censoring. While this assumption usually becomes more plausible as more baseline covariates are being adjusted for, such adjustment also raises concerns. Pre-specification of which covariates will be adjusted for (and how) is difficult, thus prompting the use of data-driven variable selection procedures, which may impede valid inferences to be drawn. The adjustment for covariates moreover adds concerns about model misspecification, and the fact that each change in adjustment set, also changes the censoring assumption and the treatment effect estimand. In this paper, we discuss these concerns and propose a simple variable selection strategy that aims to produce a valid test of the null in large samples. The proposal can be implemented using off-the-shelf software for (penalized) Cox regression, and is empirically found to work well in simulation studies and real data analyses.