title: Eliminating bias in randomized cluster trials with correlated binomial outcomes.
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.
authors.affiliation: Lehigh Valley Hospital, School of Nursing, Allentown, PA 181040, USA. jreed8341@aol.com
authors.forename: J F
authors.initials: JF
authors.lastname: Reed
mesh_terms: D000465:Algorithms; D000818:Animals; D002648:Child; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D009716:Numerical Analysis, Computer-Assisted; D011897:Random Allocation; D016032:Randomized Controlled Trials as Topic; D051381:Rats; D015983:Selection Bias; D012907:Smoking / Q000453:epidemiology; D012984:Software