Defense Date


Document Type


Degree Name

Doctor of Philosophy



First Advisor

Robert Johnson


Over the past few decades, Cluster Randomized Trials (CRT) have become a design of choice in many research areas. One of the most critical issues in planning a CRT is to ensure that the study design is sensitive enough to capture the intervention effect. The assessment of power and sample size in such studies is often faced with many challenges due to several methodological difficulties. While studies on power and sample size for cluster designs with one and two levels are abundant, the evaluation of required sample size for three-level designs has been generally overlooked. First, the nesting effect introduces more than one intracluster correlation into the model. Second, the variance structure of the estimated treatment difference is more complicated. Third, sample size results required for several levels are needed. In this work, we developed sample size and power formulas for the three-level data structures based on the generalized linear mixed model approach. We derived explicit and general power and sample size equations for detecting a hypothesized effect on continuous Gaussian outcomes and binary outcomes. To confirm the accuracy of the formulas, we conducted several simulation studies and compared the results. To establish a connection between the theoretical formulas and their applications, we developed a SAS user-interface macro that allowed the researchers to estimate sample size for a three-level design for different scenarios. These scenarios depend on which randomization level is assigned and whether or not there is an interaction effect.


© The Author

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

December 2010

Included in

Biostatistics Commons