Literature DB >> 21900289

The win ratio: a new approach to the analysis of composite endpoints in clinical trials based on clinical priorities.

Stuart J Pocock1, Cono A Ariti, Timothy J Collier, Duolao Wang.   

Abstract

The conventional reporting of composite endpoints in clinical trials has an inherent limitation in that it emphasizes each patient's first event, which is often the outcome of lesser clinical importance. To overcome this problem, we introduce the concept of the win ratio for reporting composite endpoints. Patients in the new treatment and control groups are formed into matched pairs based on their risk profiles. Consider a primary composite endpoint, e.g. cardiovascular (CV) death and heart failure hospitalization (HF hosp) in heart failure trials. For each matched pair, the new treatment patient is labelled a 'winner' or a 'loser' depending on who had a CV death first. If that is not known, only then they are labelled a 'winner' or 'loser' depending on who had a HF hosp first. Otherwise they are considered tied. The win ratio is the total number of winners divided by the total numbers of losers. A 95% confidence interval and P-value for the win ratio are readily obtained. If formation of matched pairs is impractical then an alternative win ratio can be obtained by comparing all possible unmatched pairs. This method is illustrated by re-analyses of the EMPHASIS-HF, PARTNER B, and CHARM trials. The win ratio is a new method for reporting composite endpoints, which is easy to use and gives appropriate priority to the more clinically important event, e.g. mortality. We encourage its use in future trial reports.

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Year:  2011        PMID: 21900289     DOI: 10.1093/eurheartj/ehr352

Source DB:  PubMed          Journal:  Eur Heart J        ISSN: 0195-668X            Impact factor:   29.983


  81 in total

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

Authors:  Ionut Bebu; John M Lachin
Journal:  Biostatistics       Date:  2015-09-08       Impact factor: 5.899

2.  The inverse-probability-of-censoring weighting (IPCW) adjusted win ratio statistic: an unbiased estimator in the presence of independent censoring.

Authors:  Gaohong Dong; Lu Mao; Bo Huang; Margaret Gamalo-Siebers; Jiuzhou Wang; GuangLei Yu; David C Hoaglin
Journal:  J Biopharm Stat       Date:  2020-06-17       Impact factor: 1.051

3.  Properties of composite time to first event versus joint marginal analyses of multiple outcomes.

Authors:  Ionut Bebu; John M Lachin
Journal:  Stat Med       Date:  2018-06-28       Impact factor: 2.373

4.  Days Alive and Out of Hospital: Exploring a Patient-Centered, Pragmatic Outcome in a Clinical Trial of Patients With Acute Coronary Syndromes.

Authors:  Alexander C Fanaroff; Derek Cyr; Megan L Neely; Jeffery Bakal; Harvey D White; Keith A A Fox; Paul W Armstrong; Renato D Lopes; E Magnus Ohman; Matthew T Roe
Journal:  Circ Cardiovasc Qual Outcomes       Date:  2018-12

5.  Opportunities and challenges of clinical trials in cardiology using composite primary endpoints.

Authors:  Geraldine Rauch; Bernhard Rauch; Svenja Schüler; Meinhard Kieser
Journal:  World J Cardiol       Date:  2015-01-26

6.  Clinical Trials Targeting Aging and Age-Related Multimorbidity.

Authors:  Mark A Espeland; Eileen M Crimmins; Brandon R Grossardt; Jill P Crandall; Jonathan A L Gelfond; Tamara B Harris; Stephen B Kritchevsky; JoAnn E Manson; Jennifer G Robinson; Walter A Rocca; Marinella Temprosa; Fridtjof Thomas; Robert Wallace; Nir Barzilai
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2017-03-01       Impact factor: 6.053

7.  Survival models and health sequences: discussion.

Authors:  David Oakes
Journal:  Lifetime Data Anal       Date:  2018-07-11       Impact factor: 1.588

8.  Treatment selections using risk-benefit profiles based on data from comparative randomized clinical trials with multiple endpoints.

Authors:  Brian Claggett; Lu Tian; Davide Castagno; Lee-Jen Wei
Journal:  Biostatistics       Date:  2014-08-12       Impact factor: 5.899

9.  Using Outcomes to Analyze Patients Rather than Patients to Analyze Outcomes: A Step toward Pragmatism in Benefit:risk Evaluation.

Authors:  Scott R Evans; Dean Follmann
Journal:  Stat Biopharm Res       Date:  2016-12-06       Impact factor: 1.452

Review 10.  Learning from recent trials and shaping the future of acute heart failure trials.

Authors:  Robert J Mentz; Gary Michael Felker; Tariq Ahmad; William Frank Peacock; Bertram Pitt; Mona Fiuzat; Aldo P Maggioni; Mihai Gheorghiade; Yuki Ando; Stuart J Pocock; Faiez Zannad; Christopher M O'Connor
Journal:  Am Heart J       Date:  2013-09-13       Impact factor: 4.749

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