Volume 36, Issue 15
Research Article

Weighted win loss approach for analyzing prioritized outcomes

Xiaodong Luo

Corresponding Author

E-mail address: xiaodong.luo@sanofi.com

Research and Development, Sanofi US, Bridgewater, 08807 NJ, U.S.A.

Correspondence to: Xiaodong Luo, Department of Biostatistics and Programming, Research and Development, Sanofi US, Bridgewater, NJ 08807, U.S.A.

E‐mail: xiaodong.luo@sanofi.com

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Junshan Qiu

Division of Biometrics I, Office of Biostatistics, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, 20993 U.S.A.

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Steven Bai

Division of Biometrics I, Office of Biostatistics, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, 20993 U.S.A.

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Hong Tian

Janssen Research and Development, Raritan, NJ 08869 U.S.A.

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First published: 26 March 2017
Citations: 16
disclaimer: This paper reflects the views of the authors and should not be construed to represent FDAs views or policies.

Abstract

To analyze prioritized outcomes, Buyse (2010) and Pocock et al. (2012) proposed the win loss approach. In this paper, we first study the relationship between the win loss approach and the traditional survival analysis on the time to the first event. We then propose the weighted win loss statistics to improve the efficiency of the unweighted methods. A closed‐form variance estimator of the weighted win loss statistics is derived to facilitate hypothesis testing and study design. We also calculated the contribution index to better interpret the results of the weighted win loss approach. Simulation studies and real data analysis demonstrated the characteristics of the proposed statistics. Copyright © 2017 John Wiley & Sons, Ltd.

Number of times cited according to CrossRef: 16

  • Adjusted win ratio with stratification: Calculation methods and interpretation, Statistical Methods in Medical Research, 10.1177/0962280220942558, (096228022094255), (2020).
  • The inverse-probability-of-censoring weighting (IPCW) adjusted win ratio statistic: an unbiased estimator in the presence of independent censoring, Journal of Biopharmaceutical Statistics, 10.1080/10543406.2020.1757692, (1-18), (2020).
  • The use of the win odds in the design of non-inferiority clinical trials, Journal of Biopharmaceutical Statistics, 10.1080/10543406.2020.1757690, (1-6), (2020).
  • Some Meaningful Weighted Log-Rank and Weighted Win Loss Statistics, Statistics in Biosciences, 10.1007/s12561-020-09273-4, (2020).
  • A class of proportional win‐fractions regression models for composite outcomes, Biometrics, 10.1111/biom.13382, 0, 0, (2020).
  • The win ratio: Impact of censoring and follow‐up time and use with nonproportional hazards, Pharmaceutical Statistics, 10.1002/pst.1977, 19, 3, (168-177), (2019).
  • Choosing primary endpoints for clinical trials of health care interventions, Contemporary Clinical Trials Communications, 10.1016/j.conctc.2019.100486, (100486), (2019).
  • The win ratio: On interpretation and handling of ties, Statistics in Biopharmaceutical Research, 10.1080/19466315.2019.1575279, (1-14), (2019).
  • On the alternative hypotheses for the win ratio, Biometrics, 10.1111/biom.12954, 75, 1, (347-351), (2018).
  • Rejoinder to “on the alternative hypotheses for the win ratio”, Biometrics, 10.1111/biom.12953, 75, 1, (352-354), (2018).
  • Graphing the Win Ratio and its components over time, Statistics in Medicine, 10.1002/sim.7895, 38, 1, (53-61), (2018).
  • The win ratio approach did not alter study conclusions and may mitigate concerns regarding unequal composite end points in kidney transplant trials, Journal of Clinical Epidemiology, 10.1016/j.jclinepi.2018.02.001, 98, (9-15), (2018).
  • Use of composite outcomes to assess risk–benefit in clinical trials, Clinical Trials, 10.1177/1740774518784010, 15, 4, (352-358), (2018).
  • Optimal Weighted Wilcoxon–Mann–Whitney Test for Prioritized Outcomes, New Frontiers of Biostatistics and Bioinformatics, 10.1007/978-3-319-99389-8_1, (3-40), (2018).
  • The stratified win ratio, Journal of Biopharmaceutical Statistics, 10.1080/10543406.2017.1397007, (1-19), (2017).
  • Generalized Pairwise Comparisons, Wiley StatsRef: Statistics Reference Online, 10.1002/9781118445112, (1-9), (2014).

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