1. A Semiparametric Bayesian Multivariate Model for Survival Probabilities After Acute Myocardial Infarction
- Author
-
Francesca Ieva, Alessandra Guglielmi, Anna Maria Paganoni, and Elena Prandoni
- Subjects
Multivariate statistics ,business.industry ,Physics::Medical Physics ,Bayesian probability ,Bayesian Statistics - Decision Sciences - Stochastic Processes ,medicine.disease ,Hierarchical generalized linear model ,Dirichlet process ,Prior probability ,Statistics ,Medicine ,Myocardial infarction ,Myocardial infarction diagnosis ,business ,Grouping Factor - Abstract
In this work, a Bayesian semiparametric multivariate model is fitted to study data related to in-hospital and 60-day survival probabilities of patients admitted to a hospital with ST-elevation myocardial infarction diagnosis. We consider a hierarchical generalized linear model to predict survival probabilities and a process indicator (time of intervention). Poisson-Dirichlet process priors, generalizing the well-known Dirichlet process, are considered for modeling the random-effect distribution of the grouping factor which is the hospital of admission.
- Published
- 2013