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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ieva F (2013) Designing and mining a multicenter observational clinical registry concerning patients with Acute Coronary Syndromes. In: Grieco, N, Marzegalli M, Paganoni AM (eds) New diagnostic, therapeutic and organizational strategies for acute coronary syndromes patients. Springer, Heidelberg
Pitman J, Yor M (1997) The two-parameter Poisson Dirichlet distribution derived from a stable subordinator. The Ann Probab 2:855–900
Plummer M (2003) JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In Proceedings of the 3rd International Workshop on Distributed Statistical Computing, pp 20–22
R Development Core Team (2009) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna.
Sethuraman J (1994) A constructive definition of Dirichlet process prior. Statistica Sinica 2: 639–650
Acknowledgements
This work is within the Strategic Program “Exploitation, integration and study of current and future health databases in Lombardia for Acute Myocardial Infarction” supported by “Ministero del Lavoro, della Salute e delle Politiche Sociali” and by “Direzione Generale Sanità - Regione Lombardia.” The authors wish to thank the Working Group for Cardiac Emergency in Milano, the Cardiology Society, and the 118 Dispatch Center.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Prandoni, E., Guglielmi, A., Ieva, F., Paganoni, A.M. (2014). A Semiparametric Bayesian Multivariate Model for Survival Probabilities After Acute Myocardial Infarction. In: Lanzarone, E., Ieva, F. (eds) The Contribution of Young Researchers to Bayesian Statistics. Springer Proceedings in Mathematics & Statistics, vol 63. Springer, Cham. https://doi.org/10.1007/978-3-319-02084-6_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-02084-6_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02083-9
Online ISBN: 978-3-319-02084-6
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)