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

Pubmed ID: 26353896

Pubmed Central ID: PMC4679075

Journal: Biostatistics (Oxford, England)

Publication Date: Jan. 1, 2016

Affiliation: The Biostatistics Center, The George Washington University, 6110 Executive Blvd., Rockville, MD 20852, USA.

MeSH Terms: Humans, Cardiovascular Diseases, Data Interpretation, Statistical, Models, Statistical, Outcome Assessment, Health Care

Grants: U01 DK098246, U01-DK-098246, U01-DK-094176

Authors: Lachin JM, Bebu I

Cite As: Bebu I, Lachin JM. Large sample inference for a win ratio analysis of a composite outcome based on prioritized components. Biostatistics 2016 Jan;17(1):178-87. Epub 2015 Sep 8.

Studies:

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.