Application of Time-to-Event Methods in the Assessment of Safety in Clinical Trials

Abstract

Since randomized controlled trials (RCT) are typically designed and powered for efficacy rather than safety, power is an important concern in the analysis of the effect of treatment on the occurrence of adverse events (AE). These outcomes are often time-to-event outcomes which will naturally be subject to right-censoring due to early patient withdrawals. In the analysis of the treatment effect on such an outcome, gains in efficiency, and thus power, can be achieved by exploiting covariate information. We apply the targeted maximum likelihood methodology to the estimation of treatment specific survival at a fixed end point for right-censored survival outcomes. This approach provides a method for covariate adjustment, that under no or uninformative censoring, does not require any additional parametric modeling assumptions, and, under informative censoring, is consistent under consistent estimation of the censoring mechanism or the conditional hazard for survival. Thus, the targeted maximum likelihood estimator has two important advantages over the Kaplan-Meier estimator: 1) It exploits covariates to improve efficiency, and 2) It is consistent in the presence of informative censoring. These properties are demonstrated through simulation studies. Extensions to the methodology are provided for non randomized post-market safety studies and also for the inclusion of time-dependent covariates.