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Related Experiment Video

Updated: Jul 15, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Sample size calculation for the Wilcoxon-Mann-Whitney test adjusting for ties.

Yan D Zhao¹, Dewi Rahardja, Yongming Qu

¹Eli Lilly and Company, Indianapolis, IN 46285, USA. yzhao@lilly.com

Statistics in Medicine

|May 10, 2007

Summary

This study introduces a new, easy-to-calculate sample size formula for the Wilcoxon-Mann-Whitney test. The method works for both tied and untied data, improving upon existing limitations.

Related Experiment Videos

Last Updated: Jul 15, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

Statistics
Biostatistics
Nonparametric Methods

Background:

Existing sample size calculation methods for the Wilcoxon-Mann-Whitney test are limited, applying only to data with or without ties, not both.
Methods for tied data often have restrictions, such as applicability to proportional odds alternatives or computational challenges.

Purpose of the Study:

To develop a unified and computationally simple sample size calculation method for the asymptotic Wilcoxon-Mann-Whitney test.
To address the limitations of existing methods by creating a formula applicable to both tied and untied data.

Main Methods:

A new closed-form sample size formula was derived for the asymptotic Wilcoxon-Mann-Whitney test.
The proposed method is designed to be easily calculable and applicable to datasets with or without ties.

Main Results:

The new sample size formula is straightforward to compute due to its closed-form nature.
Simulations confirmed that the proposed method performs well, with actual statistical powers closely matching nominal powers across different data types.

Conclusions:

The developed sample size formula offers a practical and versatile solution for researchers using the Wilcoxon-Mann-Whitney test.
This new method overcomes previous limitations, providing accurate sample size estimations for both tied and untied data scenarios.