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Multiple robust estimation of marginal structural mean models for unconstrained outcomes
Author(s) -
Babino Lucia,
Rotnitzky Andrea,
Robins James
Publication year - 2019
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12924
Subject(s) - estimator , marginal structural model , covariate , mathematics , sequence (biology) , parametric statistics , inverse probability , econometrics , contrast (vision) , construct (python library) , counterfactual thinking , statistics , computer science , observational study , artificial intelligence , bayesian probability , posterior probability , biology , genetics , philosophy , epistemology , programming language
Summary 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.