Zaslavsky Boris G
CBER, FDA, Rockville, Maryland, USA.
Pharm Stat. 2013 Jul-Aug;12(4):207-12. doi: 10.1002/pst.1572. Epub 2013 Apr 29.
The author considers studies with multiple dependent primary endpoints. Testing hypotheses with multiple primary endpoints may require unmanageably large populations. Composite endpoints consisting of several binary events may be used to reduce a trial to a manageable size. The primary difficulties with composite endpoints are that different endpoints may have different clinical importance and that higher-frequency variables may overwhelm effects of smaller, but equally important, primary outcomes. To compensate for these inconsistencies, we weight each type of event, and the total number of weighted events is counted. To reflect the mutual dependency of primary endpoints and to make the weighting method effective in small clinical trials, we use the Bayesian approach. We assume a multinomial distribution of multiple endpoints with Dirichlet priors and apply the Bayesian test of noninferiority to the calculation of weighting parameters. We use composite endpoints to test hypotheses of superiority in single-arm and two-arm clinical trials. The composite endpoints have a beta distribution. We illustrate this technique with an example. The results provide a statistical procedure for creating composite endpoints.
作者考虑了具有多个相关主要终点的研究。对多个主要终点进行假设检验可能需要规模大到难以管理的人群。由几个二元事件组成的复合终点可用于将试验规模缩减至可管理的大小。复合终点的主要困难在于不同终点可能具有不同的临床重要性，且高频变量可能掩盖较小但同样重要的主要结局的效应。为弥补这些不一致性，我们对每种事件类型进行加权，并计算加权事件的总数。为反映主要终点的相互依赖性并使加权方法在小型临床试验中有效，我们采用贝叶斯方法。我们假设多个终点的多项分布具有狄利克雷先验，并将贝叶斯非劣效性检验应用于加权参数的计算。我们在单臂和双臂临床试验中使用复合终点来检验优效性假设。复合终点具有贝塔分布。我们通过一个例子来说明该技术。结果提供了一种创建复合终点的统计程序。
