Paper Info
Title
A semiparametric bivariate probit model for joint modeling of outcomes in STEMI patients.
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 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.
Year
DOI
Venue
2014
10.1155/2014/240435
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
Keywords
Field
DocType
economics
Econometrics,Confounding,Covariate,Conditional probability distribution,Computer science,Multivariate probit model,Statistics,Unobservable
Journal
Volume
ISSN
Citations 
2014
1748-670X
1
PageRank 
References 
Authors
0.43
0
4
Name
Order
Citations
PageRank
Francesca Ieva153.03
Giampiero Marra2163.84
Anna Maria Paganoni374.45
Rosalba Radice4144.52