Empirical Bayes estimation and inference for the random effects model with binary response.
Waclawiw and Liang introduced an estimating function-based approach for estimating the parameters of the classical two-stage random effects model for longitudinal data. In the present paper, the authors conduct a case study of the general approach for the binary response setting, where the fully specified parametric two-stage model with fixed and univariate random effects has an analytically intractable likelihood. With successful convergence of the algorithm, the authors propose a fully parametric bootstrapping method for deriving empirical Bayes confidence intervals for all model parameters. The bootstrapping approach is a blend of the estimating function technique with the developments of Laird and Louis. The estimating function approach to estimation and inference provides a general framework for the analysis of a wide variety of medical data, including the setting of small and varying numbers of discrete repeated observations. An application of the methodology to the analysis of binary responses in a crossover clinical trial is presented.
Biostatistics Research Branch, National Heart, Lung and Blood Institute, Bethesda, Md. 20892.
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Waclawiw
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Liang
D001499:Bayes Theorem; D002545:Brain Ischemia / Q000188:drug therapy / Q000503:physiopathology; D002540:Cerebral Cortex / Q000187:drug effects / Q000503:physiopathology; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D004569:Electroencephalography / Q000187:drug effects; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*