A Bayesian random efiects model for survival probabilities after acute myocardial infarction

Studies of variations in health care utilization and outcome involve the analysis of multilevel clustered data, considering in particular the estimation of a cluster-speciflc adjusted response, covariates efiect 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, considering a Bayesian hierarchical generalized linear model, to analyze MOMI 2 (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 efiects 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 fltting are discussed through the use of predictive tail probabilities and Bayesian residuals.

[1]  Joseph Sedransk,et al.  Bayesian and Frequentist Methods for Provider Profiling Using Risk-Adjusted Assessments of Medical Outcomes , 2010 .

[2]  Scott L. Zeger,et al.  Generalized linear models with random e ects: a Gibbs sampling approach , 1991 .

[3]  C M Gibson,et al.  Relationship of symptom-onset-to-balloon time and door-to-balloon time with mortality in patients undergoing angioplasty for acute myocardial infarction. , 2000, JAMA.

[4]  I. Palacios,et al.  Impact of Time of Presentation on the Care and Outcomes of Acute Myocardial Infarction , 2008, Circulation.

[5]  I. Ntzoufras.  Gibbs Variable Selection using BUGS , 2002 .

[6]  S. Chib,et al.  Bayesian analysis of binary and polychotomous response data , 1993 .

[7]  M. Daniels,et al.  Hierarchical Generalized Linear Models in the Analysis of Variations in Health Care Utilization , 1999 .

[8]  S. Chib,et al.  Bayesian residual analysis for binary response regression models , 1995 .

[9]  M. Wand,et al.  General design Bayesian generalized linear mixed models , 2006, math/0606491.

[10]  A. Gelman.  Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper) , 2004 .

[11]  H. Migon,et al.  Bayesian binary regression model: an application to in-hospital death after AMI prediction , 2004 .

[12]  Andrew Thomas,et al.  WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility , 2000, Stat. Comput..

[13]  Francesca Ieva,et al.  Multilevel models for clinical registers concerning STEMI patients in a complex urban reality: a statistical analysis of MOMI2 survey , 2010 .

[14]  M. Glickman,et al.  Statistical Methods for Profiling Providers of Medical Care: Issues and Applications , 1997 .

[15]  M. Plummer,et al.  CODA: convergence diagnosis and output analysis for MCMC , 2006 .

[16]  M L Simoons,et al.  A prospective survey of the characteristics, treatments and outcomes of patients with acute coronary syndromes in Europe and the Mediterranean basin; the Euro Heart Survey of Acute Coronary Syndromes (Euro Heart Survey ACS). , 2002, European heart journal.

[17]  A. Santarelli,et al.  Patient selection to enhance the long-term benefit of first generation drug-eluting stents for coronary revascularisation procedures. Insights from a large multicentre registry. , 2009, EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology.

[18]  B. Mallick,et al.  Generalized Linear Models : A Bayesian Perspective , 2000 .

[19]  K. Chaloner,et al.  A Bayesian approach to outlier detection and residual analysis , 1988 .

[20]  Petros Dellaportas,et al.  On Bayesian model and variable selection using MCMC , 2002, Stat. Comput..