Adverse events in survival data: from clinical questions to methods for statistical analysis

When studying a novel treatment with a survival time outcome, failure can be defined to include a serious adverse event (AE) among the endpoints typically considered, for instance relapse (RL) or progression. These events act as competing risks, where the occurrence of RL as first event and the subsequent treatment change exclude the possibility of observing AE related to the treatment itself. In principle, the analysis of AE could be tackled by two different approaches: 1. the description of the observed occurrence of AE as first event: treatment ability to protect from RL has an impact on the chance of observing AE due to the competing risks action. The more the treatment protects from RL, the greater is the chance to observe an AE as first event; 2. the assessment of the treatment impact on the development of AE in patients who are RL free in time: one should consider the occurrence of AE as if RL would not exclude the possibility of observing AE related to the treatment itself. In the first part of the presentation we review the strategy of analysis for the two approaches starting from the type of clinical question of interest. Then we identify the suitable quantities and estimators according to two features, usually needed in a survival context: - the estimator should address for the presence of right censoring - the theoretical quantity and estimator should be functions of time. In the second part of the presentation we propose alternative methods, such as regression models, stratified Kaplan-Meier curves and inverse probability of censoring weighting, to relax the assumption of independence between the potential time to AE and the potential time to RL. We show through simulations that these methods overcome the problems related to the use of standard competing risks estimators in the second approach.