Asakura Koko, Hamasaki Toshimitsu, Evans Scott R
Department of Data Science, National Cerebral and Cardiovascular Center, Suita, Osaka, 565-8565, Japan.
Department of Innovative Clinical Trials and Data Science, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
Biom J. 2017 Jul;59(4):703-731. doi: 10.1002/bimj.201600026. Epub 2016 Oct 19.
We discuss group-sequential designs in superiority clinical trials with multiple co-primary endpoints, that is, when trials are designed to evaluate if the test intervention is superior to the control on all primary endpoints. We consider several decision-making frameworks for evaluating efficacy or futility, based on boundaries using group-sequential methodology. We incorporate the correlations among the endpoints into the calculations for futility boundaries and sample sizes as a function of other design parameters, including mean differences, the number of analyses, and efficacy boundaries. We investigate the operating characteristics of the proposed decision-making frameworks in terms of efficacy/futility boundaries, power, the Type I error rate, and sample sizes, while varying the number of analyses, the correlations among the endpoints, and the mean differences. We provide an example to illustrate the methods and discuss practical considerations when designing efficient group-sequential designs in clinical trials with co-primary endpoints.
我们讨论具有多个共同主要终点的优效性临床试验中的序贯分组设计，即当试验旨在评估试验干预在所有主要终点上是否优于对照时的情况。我们基于序贯分组方法的界值，考虑了几种用于评估疗效或无效性的决策框架。我们将终点之间的相关性纳入无效性界值和样本量的计算中，作为其他设计参数的函数，这些参数包括均值差异、分析次数和疗效界值。我们在改变分析次数、终点之间的相关性和均值差异的同时，从疗效/无效性界值、检验效能、I 型错误率和样本量方面研究了所提出的决策框架的操作特征。我们提供了一个例子来说明这些方法，并讨论在具有共同主要终点的临床试验中设计高效序贯分组设计时的实际考虑因素。
