Precise and accurate power of the rank-sum test for a continuous outcome. Issue 4 (3rd July 2020)

• Record Type: Journal Article
• Authors: Mollan, Katie R.; Trumble, Ilana M.; Reifeis, Sarah A.; Ferrer, Orlando; Bay, Camden P.; Baldoni, Pedro L.; Hudgens, Michael G.
• Is Part Of: Journal of biopharmaceutical statistics. Volume 30:Issue 4(2020)
• Page Start: 639
• Page End: 648
• Publication Date: 2020-07-03
• Subjects: Mann–Whitney test -- Monte Carlo simulation -- non-parametric -- power analysis -- Wilcoxon rank-sum test; Pharmacy -- Statistical methods -- Periodicals; Drugs -- Testing -- Statistical methods -- Periodicals; Biometry -- Periodicals; Biopharmaceutics -- Periodicals; Pharmacokinetics -- Periodicals; 615.19
• DOI: 10.1080/10543406.2020.1730866
• Languages: English
• ISSNs: 1054-3406

Abstract

ABSTRACT: Accurate power calculations are essential in small studies containing expensive experimental units or high-stakes exposures. Herein, power of the Wilcoxon Mann–Whitney rank-sum test of a continuous outcome is formulated using a Monte Carlo approach and defining P ( X < Y ) ≡ p as a measure of effect size, where X and Y denote random observations from two distributions hypothesized to be equal under the null. Effect size p fosters productive communications because researchers understand p = 0.5 is analogous to a fair coin toss, and p near 0 or 1 represents a large effect. This approach is feasible even without background data. Simulations were conducted comparing the empirical power approach to existing approaches by Rosner & Glynn, Shieh and colleagues, Noether, and O'Brien-Castelloe. Approximations by Noether and O'Brien-Castelloe are shown to be inaccurate for small sample sizes. The Rosner & Glynn and Shieh, Jan & Randles approaches performed well in many small sample scenarios, though both are restricted to location-shift alternatives and neither approach is theoretically justified for small samples. The empirical method is recommended and available in the R package wmwpow.