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Estimating population treatment effects from a survey sub-sample
Estimating populaton treatment effects from a survey subsample
  • Kara E Rudolph, Johns Hopkins Bloomberg School of Public Health
  • Ivan Diaz, Department of Biostatistics, Johns Hopkins School of Public Health
  • Michael Rosenblum, Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
  • Elizabeth A. Stuart, Johns Hopkins Bloomberg School of Public Health, Departments of Mental Health and Biostatistics
Date of this Version
1-1-2014
Abstract
We consider the problem of estimating an average treatment effect for a target population from a survey sub-sample. Our motivating example is generalizing a treatment effect estimated in a sub-sample of the National Comorbidity Survey Replication Adolescent Supplement to the population of U.S. adolescents. To address this problem, we evaluate easy-to-implement methods that account for both non-random treatment assignment and a non-random two-stage selection mechanism. We compare the performance of a Horvitz-Thompson estimator using inverse probability weighting (IPW) and two double robust estimators in a variety of scenarios. We demonstrate that the two double robust estimators generally outperform IPW in terms of mean-squared error even under misspecification of one of the treatment, selection, or outcome models. Moreover, the double robust estimators are easy to implement, providing an attractive alternative to IPW for applied epidemiologic researchers. We demonstrate how to apply these estimators to our motivating example.
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Citation Information
Kara E Rudolph, Ivan Diaz, Michael Rosenblum and Elizabeth A. Stuart. "Estimating population treatment effects from a survey sub-sample" Estimating populaton treatment effects from a survey subsample (2014)
Available at: http://works.bepress.com/michael_rosenblum/32/