A Bayesian Response-Adaptive Randomization Design for Clinical Trials with Survival Endpoints

Accordingly to FDA draft guidance (2010), adaptive randomization (e.g. responseadaptive (RA) randomization) has become popular in clinical research because of its flexibility and efficiency, which also have the advantage of assigning fewer patients to inferior treatment arms. The RA design based on binary outcome is commonly used in clinical trial where “success” is defined as the desired (or undesired) event occurring within (or beyond) a clinical relevant time. As patients entering into trial sequentially, only part of patients have sufficient follow-up during interim analysis. This results in a loss of information as it is unclear how patients without sufficient follow-up should be handled. Alternatively, adaptive design for survival trial was proposed for this type of trial. However, most of current practice assumes the event times following a prespecified parametric distribution. We adopt a nonparametric model of survival outcome which is robust to model of event time distribution, and then apply it to response-adaptive design. The operating characteristics of the proposed design along with parametric design are compared by simulation studies, including their robustness properties with respect to model misspecifications.