Influence Re-weighted G-Estimation

Rich Benjamin, Moodie Erica E. M. () and A. Stephens David
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Rich Benjamin: Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada
Moodie Erica E. M.: Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada
A. Stephens David: Department of Mathematics and Statistics, McGill University, Montreal, QC, Canada

The International Journal of Biostatistics, 2016, vol. 12, issue 1, 157-177

Abstract: Individualized medicine is an area that is growing, both in clinical and statistical settings, where in the latter, personalized treatment strategies are often referred to as dynamic treatment regimens. Estimation of the optimal dynamic treatment regime has focused primarily on semi-parametric approaches, some of which are said to be doubly robust in that they give rise to consistent estimators provided at least one of two models is correctly specified. In particular, the locally efficient doubly robust g-estimation is robust to misspecification of the treatment-free outcome model so long as the propensity model is specified correctly, at the cost of an increase in variability. In this paper, we propose data-adaptive weighting schemes that serve to decrease the impact of influential points and thus stabilize the estimator. In doing so, we provide a doubly robust g-estimator that is also robust in the sense of Hampel (15).

Keywords: asymptotic linearity; g-estimation; optimal dynamic treatment regimen structural nested mean models (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:ijbist:v:12:y:2016:i:1:p:157-177:n:7

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DOI: 10.1515/ijb-2015-0015

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