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

Xuan Ye () and Heng Li
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Xuan Ye: U.S. Food and Drug Administration
Heng Li: U.S. Food and Drug Administration

Statistics in Biosciences, 2023, vol. 15, issue 2, No 7, 419-429

Abstract: 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.

Keywords: Wilcoxon rank sum test; Gehan statistic; Finkelstein–Schoenfeld test; Covariate adjustment; Precision; Power (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s12561-023-09371-z

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