Statistical issues in the analysis of adverse events in time-to-event data.

The aim of this work is to shed some light on common issues in the statistical analysis of adverse events (AEs) in clinical trials, when the main outcome is a time-to-event endpoint. To begin, we show that AEs are always subject to competing risks. That is, the occurrence of a certain AE may be precluded by occurrence of the main time-to-event outcome or by occurrence of another (fatal) AE. This has raised concerns on 'informative' censoring. We show that, in general, neither simple proportions nor Kaplan-Meier estimates of AE occurrence should be used, but common survival techniques for hazards that censor the competing event are still valid, but incomplete analyses. They must be complemented by an analogous analysis of the competing event for inference on the cumulative AE probability. The commonly used incidence rate (or incidence density) is a valid estimator of the AE hazard assuming it to be time constant. An estimator of the cumulative AE probability can be derived if the incidence rate of AE is combined with an estimator of the competing hazard. We discuss less restrictive analyses using non-parametric and semi-parametric approaches. We first consider time-to-first-AE analyses and then briefly discuss how they can be extended to the analysis of recurrent AEs. We will give a practical presentation with illustration of the methods by a simple example.