Statistical methods for evaluating treatment effect in the presence of multiple time-to-event outcomes
Embargo Date
2027-02-12
OA Version
Citation
Abstract
Contemporary randomized trials frequently assess treatment effects across multiple time-to-event outcomes. In scenarios involving competing risks, prioritized outcomes, or informative censoring, alternatives to conventional methods to estimate and test for treatment effects are needed. For competing risks data, we proposed a doubly robust estimator for the difference in the restricted mean times lost to a specific cause. The estimator relies on non-parametric pseudo-observations of the cumulative incidence function, and therefore does not rely on the proportional hazard assumption. We evaluated the performance of the estimator in different scenarios of model misspecification. We applied the estimator to compare the event-free time lost to disease progression in the POPLAR and OAK studies for non-small-cell lung cancer. For prioritized time-to-event outcomes, we compared the performance of novel tests that prioritize events with higher clinical importance to traditional tests that do not. None of the tests was uniformly best when component-wise treatment effects varied. As these tests differ in how they characterize the treatment effect over the entire disease course, we proposed a generalizable framework to quantify the information used and ignored by each test. Under the Gumbel survival copula model, we also derived analytically the true value of the treatment effect corresponding to each test. We illustrated these methods using a five-component prioritized outcome in the SPRINT randomized trial. For informative censoring, we considered the issue of differential censoring between randomization groups in oncology trials. We assessed the impact of informative censoring on the treatment effect estimation, as well as on the performance of generalized log-rank tests under a delayed effect setting. We showed how to generate informative censoring data from survival copulas with piece-wise exponential marginals. We also derived the relationship between the copula rank correlation and the probability of informative censoring. We showed how to use this relationship to guide the choice of an adequate copula model to analyze informative censoring data.
Description
2024