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 interactwith patient’s condition at admission time. Moreover, they can also directly influence themortality 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.

A Semiparametric Bivariate Probit Model for Joint Modeling of Outcomes in STEMI Patients / F. Ieva, G. Marra, A.M. Paganoni, R. Radice. - In: COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE. - ISSN 1748-670X. - 2014(2014). [10.1155/2014/240435]

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

F. Ieva;
2014

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 interactwith patient’s condition at admission time. Moreover, they can also directly influence themortality 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.
Bivariate Outcome ; Latent Factors ; Acute Myocardial Infarction, Semiparametric Modeling
Settore SECS-S/01 - Statistica
Settore MED/01 - Statistica Medica
http://dx.doi.org/10.1155/2014/240435
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/233224
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