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Large sample inference for a win ratio analysis of a composite outcome based on prioritized components.

ABSTRACT: Composite outcomes are common in clinical trials, especially for multiple time-to-event outcomes (endpoints). The standard approach that uses the time to the first outcome event has important limitations. Several alternative approaches have been proposed to compare treatment versus control, including the proportion in favor of treatment and the win ratio. Herein, we construct tests of significance and confidence intervals in the context of composite outcomes based on prioritized components using the large sample distribution of certain multivariate multi-sample U-statistics. This non-parametric approach provides a general inference for both the proportion in favor of treatment and the win ratio, and can be extended to stratified analyses and the comparison of more than two groups. The proposed methods are illustrated with time-to-event outcomes data from a clinical trial.

SUBMITTER: Bebu I

PROVIDER: S-EPMC4679075 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Large sample inference for a win ratio analysis of a composite outcome based on prioritized components.

Bebu Ionut I Lachin John M JM

Biostatistics (Oxford, England) 20150908 1

Composite outcomes are common in clinical trials, especially for multiple time-to-event outcomes (endpoints). The standard approach that uses the time to the first outcome event has important limitations. Several alternative approaches have been proposed to compare treatment versus control, including the proportion in favor of treatment and the win ratio. Herein, we construct tests of significance and confidence intervals in the context of composite outcomes based on prioritized components using ...[more]

PMID: 26353896

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