Power analysis for cluster randomized trials with binary outcomes modeled by generalized linear mixed-effects models

T. Chen, N. Lu*, J. Arora, I. Katz, R. Bossarte, H. He, Y. Xia, Hui Zhang, X. M. Tu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1104-1118
Number of pages15
JournalJournal of Applied Statistics
Volume43
Issue number6
DOIs
StatePublished - Apr 25 2016

Keywords

  • GEE
  • GLIMMIX
  • ICC
  • NLMIXED and marginal models

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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