Improved Doubly Robust Estimation in Marginal Mean Models for Dynamic Regimes

Sun Hao, Ertefaie Ashkan, Lu Xin and Johnson Brent A. ()
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Sun Hao: Uber Technologies Inc, San Francisco, 94103, California, United States of America
Ertefaie Ashkan: Department of Biostatistics and Computational Biology, University of Rochester, Rochester, 14642, New York, United States of America
Lu Xin: Department of Biostatistics and Programming, Sanofi, Bridgewater, 07657, New Jersey, United States of America
Johnson Brent A.: Department of Biostatistics and Computational Biology, University of Rochester, Rochester, 14642, New York, United States of America

Journal of Causal Inference, 2020, vol. 8, issue 1, 300-314

Abstract: Doubly robust (DR) estimators are an important class of statistics derived from a theory of semiparametric efficiency. They have become a popular tool in causal inference, including applications to dynamic treatment regimes. The doubly robust estimators for the mean response to a dynamic treatment regime may be conceived through the augmented inverse probability weighted (AIPW) estimating function, defined as the sum of the inverse probability weighted (IPW) estimating function and an augmentation term. The IPW estimating function of the causal estimand via marginal structural model is defined as the complete-case score function for those subjects whose treatment sequence is consistent with the dynamic regime in question divided by the probability of observing the treatment sequence given the subject's treatment and covariate histories. The augmentation term is derived by projecting the IPW estimating function onto the nuisance tangent space and has mean-zero under the truth. The IPW estimator of the causal estimand is consistent if (i) the treatment assignment mechanism is correctly modeled and the AIPW estimator is consistent if either (i) is true or (ii) nested functions of intermediate and final outcomes are correctly modeled.Hence, the AIPW estimator is doubly robust and, moreover, the AIPW is semiparametric efficient if both (i) and (ii) are true simultaneously. Unfortunately, DR estimators can be inferior when either (i) or (ii) is true and the other false. In this case, the misspecified parts of the model can have a detrimental effect on the variance of the DR estimator. We propose an improved DR estimator of causal estimand in dynamic treatment regimes through a technique originally developed by [4] which aims to mitigate the ill-effects of model misspecification through a constrained optimization.In addition to solving a doubly robust system of equations, the improved DR estimator simultaneously minimizes the asymptotic variance of the estimator under a correctly specified treatment assignment mechanism but misspecification of intermediate and final outcome models. We illustrate the desirable operating characteristics of the estimator through Monte Carlo studies and apply the methods to data from a randomized study of integrilin therapy for patients undergoing coronary stent implantation. The methods proposed here are new and may be used to further improve personalized medicine, in general.

Keywords: causal inference; informative eligibility; missing data; treatment competing events (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:8:y:2020:i:1:p:300-314:n:12

DOI: 10.1515/jci-2020-0015

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