"Determinants and optimization of disability outcomes in multiple sclerosis: Applications of markov models and casual inference framework", Department of Epidemiology, School of Public Health. 

Committee: 

• Caterina Rosano, EPI (committee chair)
• Zongqi Xia, Neurology (advisor)
• Sonja Swanson, EPI
• Chung-Chou Chang, Biostatistics
• Kangho Suh, Pharmacy and Therapeutics

Abstract: 

Multiple sclerosis (MS) is a chronic neuroinflammatory and neurodegenerative disease that leads to disability. Improving long-term disability outcomes is a major goal of MS care. In real-world clinical settings, disability trajectories vary across patients and are shaped by both risk profiles and treatment decisions. Important gaps remain in understanding how patient characteristics influence disability trajectories and how treatment strategies can be optimized across clinical contexts. The overall goal of this dissertation is to improve understanding of determinants of disability in MS and to generate evidence for optimizing disability outcomes using longitudinal and causal inference approaches applied to real-world data.

In Aim 1, I applied multi-state Markov models to EHR-linked MS registries to evaluate how comorbidity burden influences disability transitions and trajectories. I found higher psychiatric and cardiometabolic comorbidity burden was associated with greater transition intensity toward worse disability states, lower transition intensity toward improvement, higher 5-year probability of reaching severe disability, and fewer years spent in low disability states. These findings supported a more integrated approach to MS care in which improving long-term outcomes requires attention to mental and vascular health in addition to MS itself.

In Aim 2, using the same modeling framework, I assessed the association between treatment use and disability outcomes and whether these associations varied by age. I found higher-efficacy treatment use was associated with more favorable disability outcomes, with greater benefit observed at younger ages. These findings suggest that the comparative effectiveness of treatment on disability is age-dependent and support a more individualized treatment approach.

In Aim 3, I further investigated treatment initiation timing. Using observational data and a clone-censor-weight framework to emulate a target trial, I found that delayed initiation of high-efficacy therapy was associated with increased risk of disability progression or death. Age-stratified analyses showed a consistent pattern of worse outcomes with delayed initiation. These findings underscore the universal importance of timely initiation of high-efficacy DMTs after diagnosis.

Together, this dissertation provides new evidence on the determinants and optimization of disability outcomes in MS, and helps inform future research and clinical efforts toward more comprehensive management and more individualized treatment strategies.