Department of Mathematical Sciences, University of Bath, Bath, UK.
Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.
Stat Med. 2024 Aug 15;43(18):3447-3462. doi: 10.1002/sim.10090. Epub 2024 Jun 9.
Multi-arm multi-stage (MAMS) platform trials efficiently compare several treatments with a common control arm. Crucially MAMS designs allow for adjustment for multiplicity if required. If for example, the active treatment arms in a clinical trial relate to different dose levels or different routes of administration of a drug, the strict control of the family-wise error rate (FWER) is paramount. Suppose a further treatment becomes available, it is desirable to add this to the trial already in progress; to access both the practical and statistical benefits of the MAMS design. In any setting where control of the error rate is required, we must add corresponding hypotheses without compromising the validity of the testing procedure.To strongly control the FWER, MAMS designs use pre-planned decision rules that determine the recruitment of the next stage of the trial based on the available data. The addition of a treatment arm presents an unplanned change to the design that we must account for in the testing procedure. We demonstrate the use of the conditional error approach to add hypotheses to any testing procedure that strongly controls the FWER. We use this framework to add treatments to a MAMS trial in progress. Simulations illustrate the possible characteristics of such procedures.
多臂多阶段(MAMS)平台试验有效地比较了几种治疗方法与一种共同的对照方法。至关重要的是,MAMS 设计允许根据需要进行多重性调整。例如,如果临床试验中的活性治疗组与药物的不同剂量水平或不同给药途径有关,则严格控制全错误率(FWER)至关重要。假设进一步的治疗方法可用,希望将其添加到正在进行的试验中;利用 MAMS 设计的实际和统计优势。在需要控制错误率的任何环境中,我们必须添加相应的假设,而不会影响测试过程的有效性。为了严格控制 FWER,MAMS 设计使用预先计划的决策规则,根据可用数据确定试验下一阶段的招募。添加治疗组会对设计进行计划外更改,我们必须在测试过程中对此进行说明。我们展示了如何使用条件错误方法将假设添加到任何严格控制 FWER 的测试过程中。我们使用此框架向正在进行的 MAMS 试验中添加治疗方法。模拟说明了此类程序的可能特征。