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

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

Flexible Models for Causal Inference in Medicine and Economics

The aim of the present work is the study of empirical aspects of a flexible regression procedure designed to perform causal inference, known as the Nonparametric Triangular Simultaneous Equations Model. This procedure helps to mitigate a problem that arise when the model regressors do not fulfill the exogeneity assumption. The main contributions emerge from two empirical applications, in Medicine and Economics, and a Monte Carlo simulation study. The first application involves an implementation of the triangular simultaneous equations model to assess the effects of a treatment, defined as \emph{time delay to catheterization}, on the outcome, defined in terms of survival and cardiac health, for patients with non ST-segment elevation Myocardial Infraction. The main methodological contribution consists on modeling the treatment as a continuous variable, instead of using a dichotomous variable indicating early versus late intervention, and using a flexible Generalized Additive Model for estimation and inference. The second application pursue an estimation of the class size's effect on schooling achievement (measured by Literature's test-scores), for students from sixth grades of the primary school in Uruguay. Main innovations consist on both, implementation of a flexible additive model that enables us to take into account nonlinear effects of control variables, and perform an adequate trimming of outlier observations, which are usually ignored in similar applications. Finally, the simulation study addressees the problem of weak identification in the nonparametric instrumental variable framework. In particular, it assess the performance of two alternative non-parametric estimators of the Triangular Simultaneous Equations Model when weak instruments are present. Two estimators, the Two Stage Generalized Additive Model (2SGAM) and the Bayesian Nonparametric Instrumental Variables (BNIV), are studied and compared. Simulation results support the advantages of BNIV over 2SGAM when instruments are weak. Specifically, when the concentration parameter ranges between 10 and 16, BNIV outperform 2SGAM in terms of variance. The mentioned efficiency advantage of BNIV does not imply an increment in bias