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Activity Number: 160 - SPEED: Biometrics
Type: Contributed
Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #322444 View Presentation
Title: Relative Efficiency of Unequal Versus Equal Cluster Sizes in Cluster Randomized Trials Using Generalized Estimating Equation Models
Author(s): Esther Lu*
Companies: Washington University School of Medicine
Keywords: cluster randomized trial (CRT) ; generalized estimating equation (GEE) ; relative efficiency (RE) ; working correlation structure, ; intraclass correlation coefficient (ICC) ; sample size
Abstract:

There has been growing interest in conducting cluster randomized trials. The cluster sizes are assumed to be identical across all the clusters in sample size calculation. However, equal cluster sizes are not guaranteed in practice. Therefore, the relative efficiency of unequal versus equal cluster sizes has been investigated when testing the treatment effect. One of the most important approaches to analyze a correlated data is generalized estimating equation proposed by Liang and Zeger. We utilize GEE models to test the treatment effect in a two-group comparison for continuous, binary, or count data in CRTs. We discuss three commonly used structures-independent, exchangeable, and AR(1). For each working correlation structure, we derive the simpler formula of RE with continuous, binary, and count outcomes. Finally, REs are investigated for several scenarios of cluster sizes distribution through the simulation studies. We propose the adjusted sample size due to the efficiency loss. Additionally, we also have the same proposal about the optimal sample size based on the GEE models under a fixed budget for known and unknown association parameter (?) in the working correlation structure.


Authors who are presenting talks have a * after their name.

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