Implementing double-robust estimators of causal effects

被引:72
作者
Emsley, Richard [1 ]
Lunt, Mark [2 ]
Pickles, Andrew [1 ]
Dunn, Graham [1 ]
机构
[1] Univ Manchester, Hlth Methodol Res Grp, Manchester M13 9PL, Lancs, England
[2] Univ Manchester, Arthritis Res Campaign Epidemiol Unit, Manchester M13 9PL, Lancs, England
关键词
st0149; dr. double-robust estimators; casual models; confounding; inverse probability of treatment weights; propensity score;
D O I
10.1177/1536867X0800800302
中图分类号
O1 [数学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 0701 ; 070101 ;
摘要
This article describes the implementation of a double-robust estimator for pretest-posttest studies (Lunceford and Davidian, 2004. Statistics in Medicine 23; 2937-2960) and presents a new Stata command (dr) that carries out the procedure. A double-robust estimator gives the analyst two opportunities for obtaining unbiased inference when adjusting for selection effects such as confounding by allowing for different forms of model misspecification; a double-robust estimator also can offer increased efficiency when all the models are correctly specified. We demostrate the results with a Monte Carlo simulation study, and we show how to implement the double-robust estimator on a single simulated dataset, both manually and by using the dr command.
引用
收藏
页码:334 / 353
页数:20
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