A semiparametric bivariate probit model for joint modeling of outcomes in STEMI patients

Keywords

Statistical learning
Health Analytics
Code:
39/2013
Title:
A semiparametric bivariate probit model for joint modeling of outcomes in STEMI patients
Date:
Wednesday 2nd October 2013
Author(s):
Ieva, F.; Marra, G.; Paganoni, A.M.; Radice, R.
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Abstract:
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 modelling 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 various reasons. First, latent factors associated with hospitals organization can affect the treatment efficacy and/or interact with patient s condition at admission time, then they can influence the mortality outcome. Such factors can be hardly measurable. Thus, the use of classical estimation methods will clearly result in inconsistent and biased parameter estimates. Secondly, covariate-outcomes relationships can exhibit non-linear 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.
This report, or a modified version of it, has been also submitted to, or published on
Computational and Mathematical Methods in Medicine.Vol. 2014, Article ID 240435
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