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Related Experiment Video

Updated: Jun 24, 2026

A Cost Effective and Adaptable Scratch Migration Assay

A Cost Effective and Adaptable Scratch Migration Assay

Published on: June 30, 2020

A SAS macro for a clustered permutation test.

Margaret R Stedman¹, David R Gagnon, Robert A Lew

¹Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States. mstedman2@partners.org

Computer Methods and Programs in Biomedicine

|March 27, 2009

Summary

This summary is machine-generated.

This study introduces a flexible SAS macro for the clustered permutation test, a nonparametric method ideal for analyzing correlated data in cluster randomized trials. The tool efficiently implements the 2-sample test for comparing treatment and control groups.

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Area of Science:

Biostatistics
Clinical Trials Methodology
Nonparametric Statistics

Background:

Correlated data analysis is crucial in cluster randomized trials (CRTs).
Traditional methods may not adequately address the clustered nature of data in CRTs.
Nonparametric significance testing offers robust alternatives for such data.

Purpose of the Study:

To present a flexible and efficient SAS macro for the 2-sample clustered permutation test.
To provide a practical tool for researchers analyzing correlated data from CRTs.
To facilitate the application of nonparametric methods in group-randomized studies.

Main Methods:

Development of a SAS macro implementing the 2-sample clustered permutation test.
Utilizing nonparametric permutation principles for significance testing.
Focus on handling correlated data structures inherent in clustered randomization.

Main Results:

The SAS macro provides a flexible and efficient implementation of the clustered permutation test.
The tool is designed for analyzing data from cluster randomized trials.
The macro facilitates robust statistical inference for correlated outcomes.

Conclusions:

The developed SAS macro offers a valuable resource for researchers conducting cluster randomized trials.
The clustered permutation test is an appropriate nonparametric method for correlated data.
Efficient implementation in SAS enhances the accessibility and application of this statistical test.