Document Type


Date of Degree

Spring 2017

Access Restrictions

Access restricted until 07/13/2019

Degree Name

MS (Master of Science)

Degree In


First Advisor

Ryan Carnahan


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