title: An alternative empirical likelihood method in missing response problems and causal inference.
abstract: Missing responses are common problems in medical, social, and economic studies. When responses are missing at random, a complete case data analysis may result in biases. A popular debias method is inverse probability weighting proposed by Horvitz and Thompson. To improve efficiency, Robins et al. proposed an augmented inverse probability weighting method. The augmented inverse probability weighting estimator has a double-robustness property and achieves the semiparametric efficiency lower bound when the regression model and propensity score model are both correctly specified. In this paper, we introduce an empirical likelihood-based estimator as an alternative to Qin and Zhang (2007). Our proposed estimator is also doubly robust and locally efficient. Simulation results show that the proposed estimator has better performance when the propensity score is correctly modeled. Moreover, the proposed method can be applied in the estimation of average treatment effect in observational causal inferences. Finally, we apply our method to an observational study of smoking, using data from the Cardiovascular Outcomes in Renal Atherosclerotic Lesions clinical trial. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Mathematics and Statistics, The University of Toledo, Toledo, 43606, OH, U.S.A.. Kaili.Ren@rockets.utoledo.edu.
authors.forename: Kaili
authors.initials: K
authors.lastname: Ren
authors.affiliation: Department of Medicine, The University of Toledo, Toledo, 43614, OH, U.S.A.
authors.forename: Christopher A
authors.initials: CA
authors.lastname: Drummond
authors.affiliation: Department of Medicine, The University of Toledo, Toledo, 43614, OH, U.S.A.
authors.forename: Pamela S
authors.initials: PS
authors.lastname: Brewster
authors.affiliation: Department of Medicine, The University of Toledo, Toledo, 43614, OH, U.S.A.
authors.forename: Steven T
authors.initials: ST
authors.lastname: Haller
authors.affiliation: Department of Medicine, The University of Toledo, Toledo, 43614, OH, U.S.A.
authors.forename: Jiang
authors.initials: J
authors.lastname: Tian
authors.affiliation: Department of Medicine, The University of Toledo, Toledo, 43614, OH, U.S.A.
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Cooper
authors.affiliation: Department of Mathematics and Statistics, The University of Toledo, Toledo, 43606, OH, U.S.A.
authors.forename: Biao
authors.initials: B
authors.lastname: Zhang
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D057216:Propensity Score
keywords: average treatment effect; causal inference; empirical likelihood; missing at random; observational study; propensity score