Analysis of prevention program effectiveness with clustered data using generalized estimating equations

Experimental studies of prevention programs often randomize clusters of individuals rather than individuals to treatment conditions. When the correlation among individuals within clusters is not accounted for in statistical analysis, the standard errors are biased, potentially resulting in misleadin...

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Bibliographic Details
Published inJournal of consulting and clinical psychology Vol. 64; no. 5; p. 919
Main Authors Norton, E C, Bieler, G S, Ennett, S T, Zarkin, G A
Format Journal Article
LanguageEnglish
Published United States 01.10.1996
Subjects
Adolescent
Adult
Child
Cluster Analysis
Curriculum
Data Interpretation, Statistical
Female
Health Education - statistics & numerical data
Humans
Longitudinal Studies
Male
Mental Disorders - prevention & control
Mental Disorders - psychology
Outcome Assessment (Health Care) - statistics & numerical data
Substance-Related Disorders - prevention & control
Substance-Related Disorders - psychology
Treatment Outcome
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Summary:Experimental studies of prevention programs often randomize clusters of individuals rather than individuals to treatment conditions. When the correlation among individuals within clusters is not accounted for in statistical analysis, the standard errors are biased, potentially resulting in misleading conclusions about the significance of treatment effects. This study demonstrates the generalized estimating equations (GEE) method, focusing specifically on the GEE-independent method, to control for within-cluster correlation in regression models with either continuous or binary outcomes. The GEE-independent method yields consistent and robust variance estimates. Data from project DARE, a youth substance abuse prevention program, are used for illustration.
ISSN:0022-006X
DOI:10.1037/0022-006X.64.5.919