期刊
AMERICAN JOURNAL OF EPIDEMIOLOGY
卷 173, 期 7, 页码 761-767出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/aje/kwq439
关键词
causal inference; epidemiologic methods; propensity score
资金
- Agency for Healthcare Research and Quality [3 U18 HS010397, K02 HS017950]
- National Institute of Allergy and Infectious Diseases Training in Sexually Transmitted Diseases AIDS [5 T32 AI07001]
- National Institute on Aging [RO1 AG023178, K25 AG027400]
- UNC-GSK Center of Excellence in Pharmacoepidemiology and Public Health
Doubly robust estimation combines a form of outcome regression with a model for the exposure (i.e., the propensity score) to estimate the causal effect of an exposure on an outcome. When used individually to estimate a causal effect, both outcome regression and propensity score methods are unbiased only if the statistical model is correctly specified. The doubly robust estimator combines these 2 approaches such that only 1 of the 2 models need be correctly specified to obtain an unbiased effect estimator. In this introduction to doubly robust estimators, the authors present a conceptual overview of doubly robust estimation, a simple worked example, results from a simulation study examining performance of estimated and bootstrapped standard errors, and a discussion of the potential advantages and limitations of this method. The supplementary material for this paper, which is posted on the Journal's Web site (http://aje.oupjournals.org/), includes a demonstration of the doubly robust property (Web Appendix 1) and a description of a SAS macro (SAS Institute, Inc., Cary, North Carolina) for doubly robust estimation, available for download at http://www.unc.edu/similar to mfunk/dr/.
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