Defense Date

2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Biostatistics

First Advisor

Bassam Dahman

Abstract

Propensity score matching is used in observational studies to balance baseline attributes between a treatment of interest and a control group. Propensity score matching typically relies on baseline variables, but longitudinal trends in patient characteristics can also influence treatment decisions and subsequent health outcomes. This dissertation extends standard approaches by explicitly incorporating longitudinal trajectories of key variables into the propensity score estimation process.

Trends in a longitudinal variable prior to baseline were characterized using group-based trajectory modeling. A two-step modeling approach was implemented where trajectory groups of a key variable were first estimated and then included as covariates in the propensity score model. In addition, a joint modelling approach that simultaneously integrates group-based trajectory modeling with propensity score estimation was also evaluated. Baseline propensity score matching was compared to the two methods incorporating trajectory groups through simulations mirroring key features of Veteran Affairs health systems liver disease data.

The two propensity score methods incorporating trajectory groups and baseline propensity score matching were applied in Veteran Affairs liver disease data. Propensity score methodology was used to examine the treatment effect of GLP-1RA on major adverse liver outcomes in patients with metabolic dysfunction associated steatotic liver disease with increased alcohol use.

This work includes novel methodological framework for incorporating longitudinal trends into the computation of propensity scores. The results provide practical guidance on when incorporating trajectory information improves covariate balance and treatment effect estimation. Simulations evaluate tradeoffs across methods and provide guidance on method selection in applied settings.

Rights

© The Author

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

5-8-2026

Included in

Biostatistics Commons

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