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A Non-parametric Test Based on Local Pairwise Comparisons of Patients for Single and Composite Endpoints

Author

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  • Xuan Ye

    (U.S. Food and Drug Administration)

  • Heng Li

    (U.S. Food and Drug Administration)

Abstract

In this article, we propose a method for adjusting for key prognostic factors in conducting a class of non-parametric tests based on pairwise comparison of subjects, namely Wilcoxon–Mann–Whitney test, Gehan test, and Finkelstein-Schoenfeld test. The idea is to only compare subjects who are comparable to each other in terms of these key prognostic factors and a user-defined comparability criterion. An advantage of our method is that it can enhance the precision of statistical inference and may increase study power, while controlling the type I error rate at the nominal significance level. The approach is contrasted with existing method via a hypothetical example. The proposed method demonstrates the study power gains.

Suggested Citation

  • Xuan Ye & Heng Li, 2023. "A Non-parametric Test Based on Local Pairwise Comparisons of Patients for Single and Composite Endpoints," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 419-429, July.
    • Handle: RePEc:spr:stabio:v:15:y:2023:i:2:d:10.1007_s12561-023-09371-z
    DOI: 10.1007/s12561-023-09371-z
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    References listed on IDEAS

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    1. Fei Jiang & Lu Tian & Haoda Fu & Takahiro Hasegawa & L. J. Wei, 2019. "Robust Alternatives to ANCOVA for Estimating the Treatment Effect via a Randomized Comparative Study," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(528), pages 1854-1864, October.
    2. Murdoch, Duncan J. & Tsai, Yu-Ling & Adcock, James, 2008. "P-Values are Random Variables," The American Statistician, American Statistical Association, vol. 62, pages 242-245, August.
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