In clinical trials, response-adaptive randomization (RAR) has the appealing ability to assign more subjects to better-performing treatments based on interim results. The traditional RAR strategy alters the randomization ratio on a patient-by-patient basis; this has been heavily criticized for bias due to time-trends. An alternate approach is blocked RAR, which groups patients together in blocks and recomputes the randomization ratio in a block-wise fashion; the final analysis is then stratified by block. However, the typical blocked RAR design divides patients into equal-sized blocks, which is not generally optimal. This paper presents TrialMDP, an algorithm that designs two-armed blocked RAR clinical trials. Our method differs from past approaches in that it optimizes the size and number of blocks as well as their treatment allocations. That is, the algorithm yields a policy that adaptively chooses the size and composition of the next block, based on results seen up to that point in the trial. TrialMDP is related to past works that compute optimal trial designs via dynamic programming. The algorithm maximizes a utility function balancing (i) statistical power, (ii) patient outcomes, and (iii) the number of blocks. We show that it attains significant improvements in utility over a suite of baseline designs, and gives useful control over the tradeoff between statistical power and patient outcomes. It is well suited for small trials that assign high cost to failures. We provide TrialMDP as an R package on GitHub: https://github.com/dpmerrell/TrialMDP
翻译:在临床试验中,反应适应随机化(RAR)具有吸引能力,能够将更多的对象分配到基于临时结果的更佳治疗中。传统的RAR战略改变了逐个病人的随机化比率;由于时间趋势的偏差,这一战略受到严厉批评;另一种方法被阻隔,将病人分组成区块,再以块状方式重新计算随机化比率;最后分析则以块状分解。然而,典型的被阻隔的RAR软件包设计将病人分为同等大小的区块,通常不是最佳的。本文展示了TreaminDP,这是一种设计双臂屏住RAR临床试验的算法。我们的方法不同于以往的方法,因为它优化了区块的大小和数目以及治疗分配。这就是,算法产生一种政策,根据结果将下一个区块的大小和组成调整到试验点;我们试验MDP与过去的工作有关,它通过动态程序将最佳试验设计分为不同的区域块块块块,这个算法是设计两件的效用性功能最优化平衡(i) 统计能力(ii) 和实验结果的大小(i) 在统计基准设计中,我们可以显示一个很大的用途) 的大小。