Response-adaptive randomization for recurrent event and terminal event data

Abstract

Recurrent event data are frequently collected to contrast the efficacy of different treatments. However, the recurrent event process could be stopped by a terminal event, such as death. For analyzing the recurrent event and terminal event data, joint frailty modeling has recently received considerable attention because it makes it possible to study the joint evolution over time of both recurrent and terminal event processes. For a two-arm clinical trial design based on these data sets, there has been limited research on the balanced design, let alone adaptive treatment allocation. Although balanced design is intuitively first adopted in a trial design, if one treatment is expected to be superior, it may be desirable and ethical to allocate more subjects to the effective treatment. In this talk, we propose a target response-adaptive randomization procedure for recurrent and terminal event outcomes based on the joint frailty model. The doubly adaptive biased coin design that targets some optimal allocations, is implemented. The proposed adaptive treatment allocation schemes have been shown to be capable of reducing the number of trial participants who receive inferior treatment while simultaneously retaining a comparable test power.