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Title page for ETD etd-07182015-020912


Type of Document Master's Thesis
Author Peterson, Emily Nancy
Author's Email Address emily.n.peterson@vanderbilt.edu
URN etd-07182015-020912
Title Assessment of Propensity Score Performance in Small Samples
Degree Master of Science
Department Biostatistics
Advisory Committee
Advisor Name Title
Tatsuki Koyama Committee Chair
Dan Ayers Committee Member
Nitin Jain Committee Member
Keywords
  • propensity score models
  • small sample
  • simulation
  • data reduction
Date of Defense 2015-07-07
Availability unrestricted
Abstract
BIOSTATISTICS

ASSESSMENT OF PROPENSITY SCORE PERFORMANCE

IN SMALL SAMPLES

EMILY PETERSON

Thesis under the direction of Professor Tatsuki Koyama

In observational studies, treatment selection is determined by the characteristics of the subject, and therefore, cannot be randomized. One must account for systematic differences in baseline characteristics between treatment groups. Propensity score is a subject’s probability of receiving a specific-treatment, which is conditioned on the observed baseline covariates, and is a method to account for differences in baseline characteristics between treatment groups. There has been little research on variable selection for propensity score models when dealing with restrictive sample sizes. The purpose of this study is to assess performance of propensity score models that use data reduction to allow for inclusion of all baseline covariates. The results of this simulation study showed that inclusion of baseline covariates related to both treatment and outcome yield the optimal propensity score model in small samples. In addition, penalized maximum likelihood methods in conjunction with propensity score models yield optimal type I error.

Approved ________________________________________________________ Date ____________________

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