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Updated: Jun 8, 2026

Computerized Adaptive Testing System of Functional Assessment of Stroke

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

Factors affecting power of tests for multiple binary outcomes.

Edward J Mascha¹, Peter B Imrey

¹Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA. maschae@ccf.org

Statistics in Medicine

|September 24, 2010

Summary

This study compares methods for analyzing multiple binary outcomes in clinical trials. The average effect generalized estimating equation (GEE) test is efficient when less frequent events have stronger treatment effects.

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Last Updated: Jun 8, 2026

Computerized Adaptive Testing System of Functional Assessment of Stroke

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

Area of Science:

Biostatistics
Clinical Trial Methodology
Epidemiology

Background:

Clinical studies often use primary outcomes derived from multiple correlated binary events.
Existing methods for analyzing such data include collapsed composites, event counts, individual outcome analyses with multiplicity adjustments, and multivariate tests.

Purpose of the Study:

To compare the performance of a 1-degree of freedom (1-df) distinct effects test, specifically the average effect generalized estimating equation (GEE) test, against other methods for analyzing multiple correlated binary outcomes.
To evaluate the clinical and statistical grounds for choosing among these methods.

Main Methods:

Simulated multivariate binary data using a flexible method to assess relative efficiencies of different tests.
Focused analysis on a 1-df distinct effects test averaging estimated outcome-specific treatment effects from a GEE model.
Compared the average effect GEE test with other established methods.

Main Results:

The relative efficiencies of the tests are complexly dependent on component incidences, treatment effect magnitudes, and event correlations.
Unlike other tests easily influenced by high-frequency components, the average effect GEE test is unweighted by component frequencies.
The average effect test demonstrates higher power when lower frequency components have stronger associations with treatment or predictors.

Conclusions:

The average effect GEE test may be preferred in studies where relative effects are crucial, or when lower frequency components hold greater clinical importance.
Recommendations for clinical practice are provided based on the analysis of two clinical trials.