Double robust estimator of average causal treatment effect for censored medical cost data |
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| Authors: | Xuan Wang Lauren A Beste Marissa M Maier Xiao‐Hua Zhou |
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| Institution: | 1. Department of Biostatistics, University of Washington, Seattle, WA, U.S.A.;2. Division of General Internal Medicine, VA Puget Sound Health Care System, VA National Liver Disease Database, School of Medicine, University of Washington, Seattle, WA, U.S.A.;3. Infectious Diseases Section, VA Portland Health Care System, HIV, Hepatitis, and Public Health Pathogens Program, Office of Patient Care Services, VHA, Division of Infectious Diseases, Oregon Health and Sciences University, Portland, OR, U.S.A.;4. Seattle HSR&D Center of Innovation for Veteran‐Centered and Value‐Driven Care, Department of Veterans Affairs Medical Center, Seattle, WA, U.S.A. |
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| Abstract: | 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. |
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| Keywords: | average causal treatment effect censored data double robust estimator inverse probability weighted lifetime medical cost data |
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