Literature DB >> 30206956

Graphing the Win Ratio and its components over time.

Dianne M Finkelstein1, David A Schoenfeld1.   

Abstract

Clinical trials are often designed to compare treatments on the basis of multiple outcomes. For the analysis of the treatment comparison from such a trial, in 1999, the Finkelstein-Schoenfeld test was proposed, which was a generalization of the Gehan-Wilcoxon test based on pairwise comparison of patients on a primary outcome when possible but otherwise on a secondary outcome. In 2012, Pocock and colleagues suggested an estimate based on this concept, the Win Ratio, which summarized the ratio of the number of patients who fared better versus worse on the experimental arm. However, in 2016, Oakes noted that the Win Ratio could be a function of the distribution of follow-up times of the trial. The aim of this paper is to propose an approach to representing the Win Ratio graphically in such a way that the effect of time on the estimate would be apparent. In addition, the methods are used to display the contribution of each endpoint to the composite. We apply the methods to clinical trials in cancer, cardiology, and neurology. Software is available named winRatioAnalysis in CRAN.
© 2018 John Wiley & Sons, Ltd.

Entities:  

Keywords:  Win Ratio; composite test; interval censored; joint test; survival

Year:  2018        PMID: 30206956     DOI: 10.1002/sim.7895

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  4 in total

1.  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

2.  Statistical models for composite endpoints of death and non-fatal events: a review.

Authors:  Lu Mao; KyungMann Kim
Journal:  Stat Biopharm Res       Date:  2021-07-06       Impact factor: 1.586

3.  On recurrent-event win ratio.

Authors:  Lu Mao; KyungMann Kim; Yi Li
Journal:  Stat Methods Med Res       Date:  2022-03-29       Impact factor: 2.494

4.  A class of proportional win-fractions regression models for composite outcomes.

Authors:  Lu Mao; Tuo Wang
Journal:  Biometrics       Date:  2020-10-10       Impact factor: 1.701

  4 in total

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