Assessment of dynamic treatment regimens effects on longitudinal outcomes through clustering and causal inference

This dissertation presents methodologies for summarizing treatment dose trajectories, clustering longitudinal data, and evaluating the impact of dynamic treatment regimens on clinical outcomes over time. First, we propose a method to compare disease progression across treatment dose trajectories by clustering participants with similar multi-treatment dose patterns. In this approach we use a marginal mean regression model to assess clinical outcomes over time, accounting for potential biases through inverse probability weighting within a quadratic inference function framework. Our application focuses on clinical outcomes for Parkinson’s disease patients from the Parkinson's Progression Markers Initiative (PPMI), where we track disorder progression across treatment clusters.