Abstract:
:Two features commonly exhibited by randomized trials of health promotion interventions are cluster randomization and stratification. Ignoring correlations between individuals within clusters can lead to an inflated type I error rate and hence a P-value which overstates the significance of the result. This paper compares several methods for analysing categorical data from a stratified cluster randomized trial. We propose an extension of a method from survey sampling that uses the design effect to reduce the effective sample size. We compare this with three methods from Zhang and Boos that extend the standard Cochran-Mantel-Haenszel (CMH) statistic by using appropriate covariance matrices, and with a bootstrap method. The comparison is based on empirical type I error rates from a simulation study, in which the number of clusters randomized is small, as in most public health intervention studies. The method that performs consistently well is one of the Zhang and Boos extensions of the standard CMH statistic.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Dobbins TA,Simpson JMdoi
10.1002/sim.1256subject
Has Abstractpub_date
2002-12-30 00:00:00pages
3835-46issue
24eissn
0277-6715issn
1097-0258journal_volume
21pub_type
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