We propose a Bayesian semiparametric regression model to represent mixed-type multiple outcomes concerning patients affected by Acute Myocardial Infarction. Our approach is motivated by data coming from the ST-Elevation Myocardial Infarction (STEMI) Archive, a multi-center observational prospective clinical study planned as part of the Strategic Program of Lombardy, Italy. We specifically consider a joint model for a variable measuring treatment time and in-hospital and 60-day survival indicators. One of our main motivations is to understand how the various hospitals differ in terms of the variety of information collected as part of the study. To do so we postulate a semiparametric random effects model that incorporates dependence on a location indicator that is used to explicitly differentiate among hospitals in or outside the city of Milano. The model is based on the two parameter Poisson-Dirichlet prior, also known as the Pitman-Yor process prior. We discuss the resulting posterior inference, including sensitivity analysis, and a comparison with the particular sub-model arising when a Dirichlet process prior is assumed.

A semiparametric Bayesian joint model for multiple mixed-type outcomes : an application to acute myocardial infarction / A. Guglielmi, F. Ieva, A.M. Paganoni, F.A. Quintana. - In: ADVANCES IN DATA ANALYSIS AND CLASSIFICATION. - ISSN 1862-5347. - (2016 Sep). [Epub ahead of print] [10.1007/s11634-016-0273-7]

A semiparametric Bayesian joint model for multiple mixed-type outcomes : an application to acute myocardial infarction

F. Ieva
Secondo
;
2016

Abstract

We propose a Bayesian semiparametric regression model to represent mixed-type multiple outcomes concerning patients affected by Acute Myocardial Infarction. Our approach is motivated by data coming from the ST-Elevation Myocardial Infarction (STEMI) Archive, a multi-center observational prospective clinical study planned as part of the Strategic Program of Lombardy, Italy. We specifically consider a joint model for a variable measuring treatment time and in-hospital and 60-day survival indicators. One of our main motivations is to understand how the various hospitals differ in terms of the variety of information collected as part of the study. To do so we postulate a semiparametric random effects model that incorporates dependence on a location indicator that is used to explicitly differentiate among hospitals in or outside the city of Milano. The model is based on the two parameter Poisson-Dirichlet prior, also known as the Pitman-Yor process prior. We discuss the resulting posterior inference, including sensitivity analysis, and a comparison with the particular sub-model arising when a Dirichlet process prior is assumed.
Bayesian clustering; Bayesian nonparametrics; two parameter Poisson-Dirichlet process prior; Random-effects models; random partition models; unbalanced binary outcomes
Settore SECS-S/01 - Statistica
set-2016
Article (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/434169
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