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Double robust estimator of average causal treatment effect for censored medical cost data
Author(s) -
Wang Xuan,
Beste Lauren A.,
Maier Marissa M.,
Zhou XiaoHua
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.6876
Subject(s) - estimator , causal inference , statistics , minimum variance unbiased estimator , delta method , confounding , observational study , mathematics , robust statistics , minimax estimator , econometrics , asymptotic distribution , variance (accounting) , efficient estimator , normality , computer science , economics , accounting
In observational studies, estimation of average causal treatment effect on a patient's response should adjust for confounders that are associated with both treatment exposure and response. In addition, the response, such as medical cost, may have incomplete follow‐up. In this article, a double robust estimator is proposed for average causal treatment effect for right censored medical cost data. The estimator is double robust in the sense that it remains consistent when either the model for the treatment assignment or the regression model for the response is correctly specified. Double robust estimators increase the likelihood the results will represent a valid inference. Asymptotic normality is obtained for the proposed estimator, and an estimator for the asymptotic variance is also derived. Simulation studies show good finite sample performance of the proposed estimator and a real data analysis using the proposed method is provided as illustration. Copyright © 2016 John Wiley & Sons, Ltd.