Multiple robust estimation of marginal structural mean models for unconstrained outcomes

ROBINS, JAMES; ROTNITZKY, ANDREA; BABINO, LUCIA

BIOMETRICS

WILEY-BLACKWELL PUBLISHING, INC

Lugar: Londres; Año: 2018

0006-341X

We consider estimation, from longitudinal observational data, of the parameters of marginal structural meanmodels 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 meanof the counterfactual outcome given covariate and treatment history up to each exposure time point. Most natural modelsfor such means are often incompatible. Robins et al., (2000b) proposed a parameterization of the likelihood which impliescompatible parametric models for such means. Their parameterization has not been exploited to construct DR estimatorsand 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 easyto implement as they are based on the iterative fit of a sequence of weighted regressions.