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An alternative empirical likelihood method in missing response problems and causal inference
Author(s) -
Ren Kaili,
Drummond Christopher A.,
Brewster Pamela S.,
Haller Steven T.,
Tian Jiang,
Cooper Christopher J.,
Zhang Biao
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7038
Subject(s) - inverse probability weighting , inverse probability , empirical likelihood , propensity score matching , estimator , causal inference , observational study , weighting , statistics , missing data , econometrics , robustness (evolution) , mathematics , marginal structural model , computer science , medicine , posterior probability , bayesian probability , biochemistry , chemistry , radiology , gene
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.