Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring
This work addresses a critical issue in clinical studies and epidemiology by providing a practical tool for robust treatment effect assessment under informative censoring, though it is incremental as it builds on existing meta-learner and partial identification methods.
The paper tackles the problem of censoring bias in survival analysis due to informative dropout, which biases treatment effect estimates, by proposing an assumption-lean framework that uses partial identification to derive bounds on conditional average treatment effects, helping identify effective patient subgroups despite censoring.
Dropout is common in clinical studies, with up to half of patients leaving early due to side effects or other reasons. When dropout is informative (i.e., dependent on survival time), it introduces censoring bias, because of which treatment effect estimates are also biased. In this paper, we propose an assumption-lean framework to assess the robustness of conditional average treatment effect (CATE) estimates in survival analysis when facing censoring bias. Unlike existing works that rely on strong assumptions, such as non-informative censoring, to obtain point estimation, we use partial identification to derive informative bounds on the CATE. Thereby, our framework helps to identify patient subgroups where treatment is effective despite informative censoring. We further develop a novel meta-learner that estimates the bounds using arbitrary machine learning models and with favorable theoretical properties, including double robustness and quasi-oracle efficiency. We demonstrate the practical value of our meta-learner through numerical experiments and in an application to a cancer drug trial. Together, our framework offers a practical tool for assessing the robustness of estimated treatment effects in the presence of censoring and thus promotes the reliable use of survival data for evidence generation in medicine and epidemiology.