Troy Jesse D, Simmons Ryan A
The Marcus Center for Cellular Cures, Duke University School of Medicine, USA.
Department of Biostatistics and Bioinformatics, Duke University School of Medicine, 424 Erwin Road Suite, 1102 Hock Plaza Box 2721, Durham, NC, 27710, USA.
Contemp Clin Trials Commun. 2020 Sep 24;20:100655. doi: 10.1016/j.conctc.2020.100655. eCollection 2020 Dec.
Statistical efficiency can be gained in clinical trials by using composites of time-to-event outcomes when the individual component outcomes have low event rates. However, the utility of continuous composite outcome measures is not as clear. Efficiency can be either gained or lost by using a continuous composite outcome measure depending on several factors, including the strength of correlation between the component outcomes and the size of the treatment effect on each component. In this article we review these concepts from the standpoint of planning a new trial. Statistical properties of composites formed from normally distributed continuous outcomes are discussed. An example dataset is used to demonstrate concepts and complete mathematical details are provided. Finally, a conceptual model for clinical trial design with continuous composites is proposed that could be used as a guide to evaluate the utility of a continuous composite outcome in a future trial based on existing knowledge in the therapeutic area.
当个体组成结局的事件发生率较低时，通过使用事件发生时间结局的复合指标可提高临床试验的统计效率。然而，连续复合结局指标的效用尚不清楚。使用连续复合结局指标可能会提高效率，也可能会降低效率，这取决于几个因素，包括组成结局之间的相关性强度以及对每个组成部分的治疗效果大小。在本文中，我们从规划一项新试验的角度回顾这些概念。讨论了由正态分布的连续结局形成的复合指标的统计特性。使用一个示例数据集来阐述这些概念，并提供完整的数学细节。最后，提出了一个用于连续复合指标的临床试验设计概念模型，该模型可作为基于治疗领域现有知识评估未来试验中连续复合结局效用的指南。
