Paper Info
Title
A semiparametric Bayesian joint model for multiple mixed-type outcomes: an application to acute myocardial infarction.
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.
Year
DOI
Venue
2018
10.1007/s11634-016-0273-7
Adv. Data Analysis and Classification
Keywords
Field
DocType
62F15, 62J12, 62P10, Bayesian clustering, Bayesian nonparametrics, Random partition models, Random-effects models, Two parameter Poisson-Dirichlet process prior, Unbalanced binary outcomes
Myocardial infarction,Econometrics,Random effects model,Observational study,Dirichlet process,Inference,Semiparametric regression,Clinical study,Statistics,Mathematics,Bayesian probability
Journal
Volume
Issue
ISSN
12
2
1862-5355
Citations 
PageRank 
References 
0
0.34
3
Authors
4
Name
Order
Citations
PageRank
Alessandra Guglielmi182.20
Francesca Ieva253.03
Anna Maria Paganoni374.45
Fernardo A. Quintana400.34