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The Use of Propensity Scores to Estimate Sample Selection Error in Observational Data

Pressler, Taylor R.

Abstract Details

2011, Master of Science, Ohio State University, Statistics.
While randomized controlled trials (RCT) are considered the “gold standard” for clinical studies, the use of exclusion criteria may impact the external validity of the results. It is un- known whether estimators of effect size are biased by excluding a portion of the population. However, it may be possible to use data from observational studies to estimate a difference between the population average treatment effect (PATE) of the included and excluded portions of the population, the sample selection error (SSE). We propose an estimator for the SSE and use simulation to study its properties while considering a non-constant treatment effect. We find that a doubly robust estimator that uses both propensity scores and a model for the outcome generally outperforms an estimator that solely relies on the use of propensity scores, even when model elements are misspecified.
Eloise Kaizar, Ph.D. (Advisor)
William Gardner, Ph.D. (Committee Member)

Recommended Citations

Citations

  • Pressler, T. R. (2011). The Use of Propensity Scores to Estimate Sample Selection Error in Observational Data [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1298997078

    APA Style (7th edition)

  • Pressler, Taylor. The Use of Propensity Scores to Estimate Sample Selection Error in Observational Data. 2011. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1298997078.

    MLA Style (8th edition)

  • Pressler, Taylor. "The Use of Propensity Scores to Estimate Sample Selection Error in Observational Data." Master's thesis, Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1298997078

    Chicago Manual of Style (17th edition)