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Approximating win-loss probabilities based on the overall and event-free survival functions

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  • Mao, Lu

Abstract

Despite its growing popularity for hierarchical composite endpoints, the win ratio poses a challenge for meta-analysis, as earlier studies typically do not report such measures. In the absence of subject-level data, we show how to approximate it using component-specific Kaplan–Meier curves that are almost universally reported. Given these marginal distributions, we infer the between-component association, as measured by the cross ratio, using summary data on event counts and rates. This leads to approximations of the win-loss probabilities that align closely with raw data-based estimates, as demonstrated in simulations and two case studies. The methodology is implemented in the winkm R package, publicly available on GitHub at https://lmaowisc.github.io/winkm.

Suggested Citation

  • Mao, Lu, 2025. "Approximating win-loss probabilities based on the overall and event-free survival functions," Statistics & Probability Letters, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:stapro:v:226:y:2025:i:c:s0167715225001233
    DOI: 10.1016/j.spl.2025.110478
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    References listed on IDEAS

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    1. D. Oakes, 2016. "On the win-ratio statistic in clinical trials with multiple types of event," Biometrika, Biometrika Trust, vol. 103(3), pages 742-745.
    2. Lu Mao & KyungMann Kim & Xinran Miao, 2022. "Sample size formula for general win ratio analysis," Biometrics, The International Biometric Society, vol. 78(3), pages 1257-1268, September.
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