Power analysis for cluster randomized trials with binary outcomes modeled by generalized linear mixed-effects models |
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| Authors: | T. Chen J. Arora I. Katz R. Bossarte H. He |
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| Affiliation: | 1. Department of Mathematics and Statistics, University of Toledo, Toledo, OH, USA;2. Bioinformatics and Biostatistics Shared Resource, Winship Cancer Institute of Emory University, Atlanta, GA, USA;3. Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, New York, USA;4. Department of Epidemiology, School of Public Health &5. Tropical Medicine, Tulane University, New Orleans, LA, USA |
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| Abstract: | Power analysis for cluster randomized control trials is difficult to perform when a binary response is modeled using the generalized linear mixed-effects model (GLMM). Although methods for clustered binary responses exist such as the generalized estimating equations, they do not apply to the context of GLMM. Also, because popular statistical packages such as R and SAS do not provide correct estimates of parameters for the GLMM for binary responses, Monte Carlo simulation, a popular ad-hoc method for estimating power when the power function is too complex to evaluate analytically or numerically, fails to provide correct power estimates within the current context as well. In this paper, a new approach is developed to estimate power for cluster randomized control trials when a binary response is modeled by the GLMM. The approach is easy to implement and seems to work quite well, as assessed by simulation studies. The approach is illustrated with a real intervention study to reduce suicide reattempt rates among US Veterans. |
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| Keywords: | GEE GLIMMIX ICC NLMIXED and marginal models |
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