A hierarchical random-effects model for survival in patients with Acute Myocardial Infarction

Studies of variations in health care utilization and outcome involve the analysis of multilevel clustered data. These analyses involve estimation of a cluster-specific adjusted response, covariates effect and components of variance. Beyond reporting on the extent of observed variations, these studies examine the role of contributing factors including patients and providers characteristics. In addition, they may assess the relationship between health-care process and outcomes. In this article we present a case-study, considering firstly a Hierarchical Generalized Linear Model (HGLM) formulation, then a semi-parametric Dirichlet ProcessMixtures (DPM), and propose their application to the analysis of MOMI2 (MOnth MOnitoring Myocardial Infarction in MIlan) study on patients admitted with ST-Elevation Myocardial Infarction diagnosys. We develop a Bayesian approach to fitting data using Markov Chain Monte Carlo methods and discuss some issues about model fitting.