Date of Degree
Access restricted until 07/13/2019
MS (Master of Science)
Objective: To assess the ability of propensity score methods to maintain covariate balance and minimize bias in the estimation of treatment effect in a time-to-event setting.
Data Sources: Generated simulation model
Study Design: Simulation study
Data Collection: 6 scenarios with varying covariate relationships to treatment and outcome with 2 different censoring prevalences
Principal Findings: As time lapses, balance achieved at baseline through propensity score methods between treated and untreated groups trends toward imbalance, particularly in settings with high rates of censoring. Furthermore, there is a high degree of variability in the performance of different propensity score models with respect to effect estimation.
Conclusions: Caution should be used when incorporating propensity score analysis methods in survival analyses. In these settings, if model over-parameterization is a concern, Cox regression stratified on propensity score matched pairs often provides more accurate conditional treatment effect estimates than those of unstratified matched or IPT weighted Cox regression models.
covariate balance, matching, Propensity score, simulation
vii, 49 pages
Includes bibliographical references (page 49).
Copyright © 2017 Jessica Hinman
Available for download on Saturday, July 13, 2019