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Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 240435, 7 pages
Research Article

A Semiparametric Bivariate Probit Model for Joint Modeling of Outcomes in STEMI Patients

1Department of Mathematics, Università Degli Studi di Milano, Via Saldini 50, 20133 Milano, Italy
2Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK
3Modeling and Scientific Computing (MOX), Department of Mathematics, Politecnico di Milano, Via Bonardi 9, 20133 Milano, Italy
4Department of Economics, Mathematics and Statistics, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK

Received 8 January 2014; Revised 25 February 2014; Accepted 10 March 2014; Published 1 April 2014

Academic Editor: Guang Wu

Copyright © 2014 Francesca Ieva et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In this work we analyse the relationship among in-hospital mortality and a treatment effectiveness outcome in patients affected by ST-Elevation myocardial infarction. The main idea is to carry out a joint modeling of the two outcomes applying a Semiparametric Bivariate Probit Model to data arising from a clinical registry called STEMI Archive. A realistic quantification of the relationship between outcomes can be problematic for several reasons. First, latent factors associated with hospitals organization can affect the treatment efficacy and/or interact with patient’s condition at admission time. Moreover, they can also directly influence the mortality outcome. Such factors can be hardly measurable. Thus, the use of classical estimation methods will clearly result in inconsistent or biased parameter estimates. Secondly, covariate-outcomes relationships can exhibit nonlinear patterns. Provided that proper statistical methods for model fitting in such framework are available, it is possible to employ a simultaneous estimation approach to account for unobservable confounders. Such a framework can also provide flexible covariate structures and model the whole conditional distribution of the response.