Eliminating Bias in Randomized Cluster Trials With Correlated Binomial Outcomes.

Publication/Presentation Date

2-1-2000

Abstract

Clustered or correlated samples with binary data are frequently encountered in biomedical studies. The clustering may be due to repeated measurements of individuals over time or may be due to subsampling of the primary sampling units. Individuals in the same cluster tend to behave more alike than individuals who belong to different clusters. This exhibition of intracluster correlation decreases the amount of information about the effect of the intervention. In the analysis of randomized cluster trials one must adjust the variance of estimator of the mean for the effect of the positive intraclass correlation p;. We review selected alternative methods to the typical Pearson's chi2 analysis, illustrate these alternatives, and out line an alternative analysis algorithm. We have written and tested a FORTRAN program that produces the statistics outlined in this paper. The program is available in an executable format and is available from the author on request.

Volume

61

Issue

2

First Page

119

Last Page

123

ISSN

0169-2607

Disciplines

Medical Sciences | Medicine and Health Sciences

PubMedID

10661397

Department(s)

Department of Medicine

Document Type

Article

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