Estimability and Likelihood Inference for Generalized Linear Mixed Models Using Data Cloning

Abstract

Maximum likelihood estimation for Generalized Linear Mixed Models (GLMM), an important class of statistical models with substantial applications in epidemiology, medical statistics, and many other fields, poses significant computational difficulties. In this article, we use data cloning, a simple computational method that exploits advances in Bayesian computation, in particular the Markov Chain Monte Carlo method, to obtain maximum likelihood estimators of the parameters in these models. This method also leads to a simple estimator of the asymptotic variance of the maximum likelihood estimators. Determining estimability of the parameters in a mixed model is, in general, a very difficult problem. Data cloning provides a simple graphical test to not only check if the full set of parameters is estimable but also, and perhaps more importantly, if a specified function of the parameters is estimable. One of the goals of mixed models is to predict random effects. We suggest a frequentist method to obtain prediction intervals for random effects. We illustrate data cloning in the GLMM context by analyzing the Logistic–Normal model for over-dispersed binary data, and the Poisson–Normal model for repeated and spatial counts data. We consider Normal–Normal and Binary–Normal mixture models to show how data cloning can be used to study estimability of various parameters. We contend that whenever hierarchical models are used, estimability of the parameters should be checked before drawing scientific inferences or making management decisions. Data cloning facilitates such a check on hierarchical models.