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Some Meaningful Weighted Log-Rank and Weighted Win Loss Statistics

Author

Listed:
  • Xiaodong Luo

    (Sanofi US)

  • Hui Quan

    (Sanofi US)

Abstract

Weighted log-rank statistics and recently weighted win loss statistics are often used to test the null hypothesis that the treatment group and the control group have no difference. However, they usually do not provide meaningful treatment effect estimates. This paper studies a few weighted log-rank statistics and weighted win loss statistics that will provide meaningful treatment effect estimates.

Suggested Citation

  • Xiaodong Luo & Hui Quan, 2020. "Some Meaningful Weighted Log-Rank and Weighted Win Loss Statistics," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(2), pages 216-224, July.
    • Handle: RePEc:spr:stabio:v:12:y:2020:i:2:d:10.1007_s12561-020-09273-4
    DOI: 10.1007/s12561-020-09273-4
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    References listed on IDEAS

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    1. Song Yang & Ross Prentice, 2010. "Improved Logrank-Type Tests for Survival Data Using Adaptive Weights," Biometrics, The International Biometric Society, vol. 66(1), pages 30-38, March.
    2. 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.
    3. Lu Mao, 2019. "On the alternative hypotheses for the win ratio," Biometrics, The International Biometric Society, vol. 75(1), pages 347-351, March.
    4. Xiaodong Luo & Hong Tian & Surya Mohanty & Wei Yann Tsai, 2015. "An alternative approach to confidence interval estimation for the win ratio statistic," Biometrics, The International Biometric Society, vol. 71(1), pages 139-145, March.
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    Cited by:

    1. Bo Huang & Naitee Ting, 2020. "Introduction to Special Issue on ‘Statistical Methods for Cancer Immunotherapy’," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(2), pages 79-82, July.

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