1 Model Based Drug Development, Statistical Decision Sciences, Janssen Research & Development, LLC, Titusville, NJ, USA.
2 Department of Biostatistics, MD Anderson Cancer Center, Houston, TX, USA.
Clin Trials. 2017 Oct;14(5):432-440. doi: 10.1177/1740774517692302. Epub 2017 Feb 1.
Randomizing patients among treatments with equal probabilities in clinical trials is the established method to obtain unbiased comparisons. In recent years, motivated by ethical considerations, many authors have proposed outcome adaptive randomization, wherein the randomization probabilities are unbalanced, based on interim data, to favor treatment arms having more favorable outcomes. While there has been substantial controversy regarding the merits and flaws of adaptive versus equal randomization, there has not yet been a systematic simulation study in the multi-arm setting. A simulation study was conducted to evaluate four different Bayesian adaptive randomization methods and compare them to equal randomization in five-arm clinical trials. All adaptive randomization methods included an initial burn-in with equal randomization and some combination of other modifications to avoid extreme randomization probabilities. Trials either with or without a control arm were evaluated, using designs that may terminate arms early for futility and select one or more experimental treatments at the end. The designs were evaluated under a range of scenarios and sample sizes. For trials with a control arm and maximum same size 250 or 500, several commonly used adaptive randomization methods have very low probabilities of correctly selecting a truly superior treatment. Of those studied, the only adaptive randomization method with desirable properties has a burn-in with equal randomization and thereafter randomization probabilities restricted to the interval 0.10-0.90. Compared to equal randomization, this method has a favorable sample size imbalance but lower probability of correctly selecting a superior treatment. In multi-arm trials, compared to equal randomization, several commonly used adaptive randomization methods give much lower probabilities of selecting superior treatments. Aside from randomization method, conducting a multi-arm trial without a control arm may lead to very low probabilities of selecting any superior treatments if differences between the treatment success probabilities are small.
在临床试验中，以相等的概率将患者随机分配到不同治疗组是获得无偏比较的既定方法。近年来，出于伦理考虑，许多作者提出了基于中期数据的适应性随机化，即根据中期数据，将随机化概率不平衡，以有利于疗效更优的治疗组。虽然关于适应性随机化与均等随机化的优缺点存在很大争议，但在多臂环境中尚未进行系统的模拟研究。本研究进行了一项模拟研究，以评估四种不同的贝叶斯适应性随机化方法，并将其与五臂临床试验中的均等随机化进行比较。所有适应性随机化方法都包括初始均等随机化的“预热期”，以及避免极端随机化概率的其他一些修改。评估了有无对照臂的试验，使用可能因无效而提前终止手臂和在试验结束时选择一种或多种实验性治疗的设计。这些设计在一系列场景和样本量下进行了评估。对于有对照臂和最大相同样本量为 250 或 500 的试验，几种常用的适应性随机化方法正确选择真正优越治疗的概率非常低。在所研究的方法中，唯一具有理想特性的适应性随机化方法是在均等随机化的“预热期”后，将随机化概率限制在 0.10-0.90 之间。与均等随机化相比，这种方法具有有利的样本量不平衡性，但正确选择优越治疗的概率较低。与均等随机化相比，在多臂试验中，几种常用的适应性随机化方法正确选择优越治疗的概率要低得多。除了随机化方法外，如果治疗成功率差异较小，不设置对照臂的多臂试验可能导致选择任何优越治疗的概率非常低。
