Construction of patient trajectories to model clinical trial outcomes: Application to Myasthenia Gravis.

Accurate prediction of patient outcomes in clinical trials is crucial for timely assessment of treatment efficacy. This study introduces a novel approach to predict patient response by constructing temporal trajectories from longitudinal clinical data. We aim to extrapolate these trajectories to forecast individual outcomes and identify when new patients align with established response patterns. Utilizing data from the MGTX trial of myasthenia gravis patients, we evaluate the predictability of these trajectories and discuss potential confounding factors. Furthermore, our analysis yields an automatic clustering of patients based on treatment success, revealing potential associations with age and smoking status.