Multiple robust estimation of marginal structural mean models for unconstrained outcomes

Biometrics
Lucia BabinoJames Robins

Abstract

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.

References

Aug 24, 2000·Epidemiology·J M RobinsB Brumback
Jan 13, 2006·Biometrics·Heejung Bang, James M Robins
Sep 7, 2007·Clinical Infectious Diseases : an Official Publication of the Infectious Diseases Society of America·Maya L PetersenDavid R Bangsberg
Dec 1, 2001·Journal of the American Statistical Association·S A MurphyUNKNOWN CPPRG
Mar 30, 2011·Journal of Consulting and Clinical Psychology·Tyler J VanderWeeleJohn T Cacioppo
Dec 1, 2008·Journal of the American Statistical Association·Stijn VansteelandtJames M Robins
Jan 1, 2010·The International Journal of Biostatistics·Susan Gruber, Mark J van der Laan
Jan 1, 2010·The International Journal of Biostatistics·Liliana OrellanaJames M Robins
Apr 14, 2012·The International Journal of Biostatistics·Wenjing Zheng, Mark J van der Laan
Feb 13, 2018·Biometrika·J MolinaJ M Robins

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