An Approach to Design Adaptive Clinical Trials With Time-to-Event Outcomes Based on a General Bayesian Posterior Distribution.

Abstract

Clinical trials are an integral component of medical research. Trials require careful design to, for example, maintain the safety of participants and to use resources efficiently. Adaptive clinical trials are often more efficient and ethical than standard or non-adaptive trials because they can require fewer participants, target more promising treatments, and stop early with sufficient evidence of effectiveness or harm. The design of adaptive trials is usually undertaken via simulation, which requires assumptions about the data-generating process to be specified a priori. Unfortunately, if such assumptions are misspecified, then the resulting trial design may not perform as expected, leading to, for example, reduced statistical power or an increased Type I error. Motivated by a clinical trial of a vaccine to protect against gastroenteritis in infants, we propose an approach to design adaptive clinical trials with time-to-event outcomes without needing to explicitly define the data-generating process. To facilitate this, we consider trial design within a general Bayesian framework where inference about the treatment effect is based on the partial likelihood. As a result, inference is robust to the form of the baseline hazard function, and we exploit this property to undertake trial design when the data-generating process is only implicitly defined. The benefits of this approach are demonstrated via an illustrative example and via redesigning our motivating clinical trial.