Power and sample size calculation for the win odds test: application to an ordinal endpoint in COVID-19 trials

• Published in: Journal of biopharmaceutical statistics Vol. 31; no. 6; pp. 765 - 787
• Main Authors: Gasparyan, Samvel B., Kowalewski, Elaine K., Folkvaljon, Folke, Bengtsson, Olof, Buenconsejo, Joan, Adler, John, Koch, Gary G.
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 02.11.2021; Taylor & Francis Ltd
• Subjects: COVID-19; effect size; Fligner-Policello; Humans; Mann-Whitney; number needed to treat; power; Research Design; Sample Size; SAS software; Somers' D C/R; Wilcoxon rank-sum; Win odds; COVID-19; Somers’ D C/R; Mann-Whitney; SAS software; Wilcoxon rank-sum; Win odds; Fligner-Policello; power; effect size; number needed to treat; sample size
• ISSN: 1054-3406; 1520-5711
• DOI: 10.1080/10543406.2021.1968893

The win odds is a distribution-free method of comparing locations of distributions of two independent random variables. Introduced as a method for analyzing hierarchical composite endpoints, it is well suited to be used in the analysis of ordinal scale endpoints in COVID-19 clinical trials. For a single outcome, we provide power and sample size calculation formulas for the win odds test. We also provide an implementation of the win odds analysis method for a single ordinal outcome in a commonly used statistical software to make the win odds analysis fully reproducible.