A Bayesian random-effects model for survival probabilities after acute myocardial infarction

Studies of variations in health care utilization and outcome involve the analysis of multi- level clustered data, considering in particular the estimation of a cluster-specifc adjusted response, covariates effect and components of variance. Besides reporting on the extent of observed variations, those studies quantify 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, con- sidering a Bayesian hierarchical generalized linear model, to analyze MOMI2 (Month Monitoring Myocardial Infarction in Milan) data on patients admitted with ST-elevation myocardial infarction diagnosis; both clinical registries and administrative databanks were used to predict survival probabilities. The major contributions of the paper consist in the comparison of the performance of the health care providers, as well as in the assessment of the role of patients' and providers' characteristics on survival outcome. In particular, we obtain posterior estimates of the regression parameters, as well as of the random effects parameters (the grouping factor is the hospital the patients were admitted to), through an MCMC algorithm. The choice of covariates is achieved in a Bayesian fashion as a preliminary step. Some issues about model fitting are discussed through the use of predictive tail probabilities and Bayesian residuals.