Random-Effect Prediction Approach for Clustered Binominal Data

Clustered data may have not only a correlation within-cluster but also a heterogeneity between-cluster. Thus, various random-effect models including generalized linear mixed models(GLMMs) have been widely used. However, the analysis of heterogeneity has been less studied. For this the prediction of random effects is very useful. In this paper we show a general framework how to model the clustered binominal data, and to estimate parameters and to predict random effects via SAS PROC NLMIXED. Thus, we illustrate the analyses with a real data set obtained from different 8 clinics for investigating a treatment effect. We demonstrate using this data set how an appropriate model can be selected via well-known model-selection criteria. We also present a plot on predicted random effects for investigating practically a potential heterogeneity over clinics.