DOI
https://doi.org/10.25772/92K5-GY64
Defense Date
2006
Document Type
Dissertation
Degree Name
Doctor of Philosophy
Department
Biostatistics
First Advisor
Dr. Robert E. Johnson
Abstract
Cluster randomized trials (CRT) are comparative studies designed to evaluate interventions where the unit of analysis and randomization is the cluster but the unit of observation is individuals within clusters. Typically such designs involve a limited number of clusters and thus the variation between clusters is left uncontrolled. Experimental designs and analysis strategies that minimize this variance are required. In this work we focus on the CRT with pre-post intervention measures. By incorporating the baseline measure into the analysis, we can effectively reduce the variance of the treatment effect. Well known methods such as adjustment for baseline as a covariate and analysis of differences of pre and post measures are two ways to accomplish this. An alternate way of incorporating baseline measures in the data analysis is to order the clusters on baseline means and pairmatch the two clusters with the smallest means, pair-match the next two, and so on. Our results show that matching on baseline helps to control the between cluster variation when there is a high correlation between the pre-post measures. Six cases of designs and analysis are evaluated by comparing the variance of the treatment effect and the power of related hypothesis tests. We observed that - given our assumptions - the adjusted analysis for baseline as a covariate without pair-matching is the best choice in terms of variance. Future work may reveal that other matching schemes that reflect the natural clustering of experimental units could reduce the variance and increase the power over the standard methods.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
June 2008