Artículo
Multiple robust estimation of marginal structural mean models for unconstrained outcomes
Fecha de publicación:
03/2019
Editorial:
Wiley Blackwell Publishing, Inc
Revista:
Biometrics
ISSN:
0006-341X
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
We consider estimation, from longitudinal observational data, of the parameters of marginal structural mean models for unconstrained outcomes. Current proposals include inverse probability of treatment weighted and double robust (DR) estimators. A difficulty with DR estimation is that it requires postulating a sequence of models, one for the each mean of the counterfactual outcome given covariate and treatment history up to each exposure time point. Most natural models for such means are often incompatible. Robins et al., (2000b) proposed a parameterization of the likelihood which implies compatible parametric models for such means. Their parameterization has not been exploited to construct DR estimators and one goal of this article is to fill this gap. More importantly, exploiting this parameterization we propose a multiple robust (MR) estimator that confers even more protection against model misspecification than DR estimators. Our methods are easy to implement as they are based on the iterative fit of a sequence of weighted regressions.
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Articulos(IEGEBA)
Articulos de INSTITUTO DE ECOLOGIA, GENETICA Y EVOLUCION DE BS. AS
Articulos de INSTITUTO DE ECOLOGIA, GENETICA Y EVOLUCION DE BS. AS
Citación
Babino, Lucía; Rotnitzky, Andrea Gloria; Robins, James; Multiple robust estimation of marginal structural mean models for unconstrained outcomes; Wiley Blackwell Publishing, Inc; Biometrics; 75; 1; 3-2019; 90-99
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