Document Type


Date of Degree

Spring 2017

Access Restrictions

Access restricted until 07/13/2021

Degree Name

MS (Master of Science)

Degree In


First Advisor

Carnahan, Ryan

First Committee Member

Ammann, Eric

Second Committee Member

Jones, Mike


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.

Public Abstract

The gold-standard for establishing causation in biomedical and epidemiologic studies is the randomized-controlled trial, in which all subjects are randomly selected to be in either the experimental arm (receiving the treatment under evaluation) or the control arm (receiving an alternative therapy). This methodology is ideal due to the fact that all subjects have an equal probability of being in each arm, regardless of their underlying personal factors. In this setting, if a sufficient number of subjects are enrolled in a study, these underlying factors should be balanced in each study group and thus should not unduly bias the results of the study.

In practice, however, there are many scenarios in which conducting a randomized-controlled trial is difficult. For this reason, statistical ways of mimicking this randomization in observational studies, where the investigator has no control over which subjects receive the treatment in question, have become increasingly important. One method involves estimating the probability that a subject would receive the treatment, termed their propensity score. These scores may then be incorporated into the analysis of the study results in various ways in an attempt to balance the underlying subject-specific factors across the treated and untreated study groups.

This simulation study aims to evaluate the extent to which propensity score methods accurately estimate the effect of a treatment while maintaining balance across underlying factors in the treated and non-treated groups in a setting where the outcome of interest is the amount of time lapsed prior to a particular event.


covariate balance, matching, Propensity score, simulation


vii, 49 pages


Includes bibliographical references (page 49).


Copyright © 2017 Jessica Hinman

Available for download on Tuesday, July 13, 2021