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SAS macros for testing statistical mediation in data with binary mediators or outcomes.

Srichand Jasti¹, William N Dudley, Eva Goldwater

¹Emma Eccles Jones Nursing Research Center, University of Utah College of Nursing, Salt Lake City, UT 84112, USA. srichand@gmail.com

Nursing Research

|March 19, 2008

Summary

This summary is machine-generated.

Statistical mediation analysis is now accessible for binary data in behavioral health. New SAS macros quantify mediation effects, showing sleep disturbance mediated 23.34% of the pain-fatigue relationship.

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Area of Science:

Behavioral Health Sciences
Biostatistics
Psychology

Background:

Statistical mediation analysis traditionally focused on continuous variables.
Prevention studies increasingly involve binary mediators or outcomes.
Addressing mediation with binary data is crucial for advancing behavioral health research.

Purpose of the Study:

To introduce SAS macros for statistical mediation analysis with binary and continuous data.
To provide practical tools for researchers analyzing complex mediation models.
To facilitate the application of mediation techniques in behavioral health studies.

Main Methods:

Mediation analysis methodology for binary outcomes/mediators is briefly explained.
Newly developed SAS macros are utilized for data analysis.
A sample of 84 participants with pain was analyzed to test the macros.

Main Results:

Sleep disturbance was identified as a significant mediator between pain and fatigue.
The extent of mediation by sleep disturbance was quantified at 23.34%.
The analysis demonstrated the utility of the SAS macros in identifying mediation effects.

Conclusions:

Freely available SAS macros can be downloaded from the second author's website.
A technical manual is provided for user guidance.
These macros offer a valuable resource for researchers conducting mediation analysis with binary data.