A Bayesian approach for event predictions in clinical trials with time-to-event outcomes.

Abstract

In clinical trials with time-to-event outcome as the primary endpoint, the end of study date is often based on the number of observed events, which drives the statistical power and the sample size calculation. It is of great value for study sponsors to have a good understanding of the recruitment process and the event milestones to manage the logistical tasks, which require a considerable amount of resources. The objective of the proposed statistical approach is to predict, as accurately as possible, the timing of an analysis planned once a target number of events is collected. The method takes into account the enrollment, the time to event, and the time to censor processes, using Weibull models in a Bayesian framework. We also consider a possible delay in the event reporting by the investigators, and covariates may also be included. Several metrics can be obtained, such as the probability of study completion at specific timepoints or the credible interval of the date of study completion. The approach was applied to oncology trials, with progression-free survival as primary outcome. A retrospective analysis shows the accuracy of the approach on these examples, as well as the benefit of updating the predictive probability of study completion as data are accumulating or new information becomes available. We also evaluated the performances of the proposed method in a comprehensive simulation study.