Weighted analysis of composite endpoints with simultaneous inference for flexible weight constraints

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• 作者：Anh Nguyen Duc and Marcel Wolbers
• 刊名：Statistics in Medicine
• 出版年：2017
• 出版时间：10 February 2017
• 年：2017
• 卷：36
• 期：3
• 页码：442-454
• 全文大小：683K
• ISSN：1097-0258

文摘

Composite endpoints are widely used as primary endpoints of randomized controlled trials across clinical disciplines. A common critique of the conventional analysis of composite endpoints is that all disease events are weighted equally, whereas their clinical relevance may differ substantially. We address this by introducing a framework for the weighted analysis of composite endpoints and interpretable test statistics, which are applicable to both binary and time-to-event data. To cope with the difficulty of selecting an exact set of weights, we propose a method for constructing simultaneous confidence intervals and tests that asymptotically preserve the family-wise type I error in the strong sense across families of weights satisfying flexible inequality or order constraints based on the theory of <mover accent="true">χ<mo>¯mo>mover>2-distributions. We show that the method achieves the nominal simultaneous coverage rate with substantial efficiency gains over Scheffé's procedure in a simulation study and apply it to trials in cardiovascular disease and enteric fever.