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Generalizing Results from Randomized Trials to Target Population via Weighting Methods Using Propensity Score

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서명/저자사항Generalizing Results from Randomized Trials to Target Population via Weighting Methods Using Propensity Score.
개인저자Chen, Ziyue.
단체저자명The Ohio State University. Statistics.
발행사항[S.l.]: The Ohio State University., 2017.
발행사항Ann Arbor: ProQuest Dissertations & Theses, 2017.
형태사항212 p.
기본자료 저록Dissertation Abstracts International 79-10B(E).
Dissertation Abstract International
ISBN9780438099012
학위논문주기Thesis (Ph.D.)--The Ohio State University, 2017.
일반주기 Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Adviser: Eloise Kaizar.
요약Randomized controlled trials (RCTs) provide strong internal validity compared with observational studies. However, selection bias threatens the external validity of randomized trials. Thus, naive RCT results may not apply to either broad general
요약We are interested in two types of target populations: finite and infinite populations. We propose a simulation framework that imitates the environment composing a randomized trial and target population, i.e., the single-layer simulation construc
요약We develop a model-free inverse probability weighted estimator (IPWE) to estimate the average treatment effect in a target population and propose several variance estimation methods, including parametric estimation methods and bootstrap-based me
요약In addition to IPWEs, we study model-based estimators including regression-based and survey regression-based estimators. With weights, the survey design-based estimators perform similarly to the regression-based estimators. However, the performa
요약We also propose estimating PSs with separate models for the treatment and control arms of the trial, rather than estimating PSs together (treatment and control groups in trials combined). We find that for many common situations, IPWEs with weigh
일반주제명Statistics.
언어영어
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