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

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