Event Prediction with Prognostic Clinical Markers by Joint Modelling

Oregon Convention Center 

Background: Efficient planning in clinical trials with time-to-event outcomes hinges on accurate timing predictions for achieving target event numbers. Traditionally, event prediction relies on simple survival models, overlooking the wealth of prognostic clinical markers. We propose a novel approach for event prediction by joint modeling the clinical markers and survival outcome.
Statistical Methods: The proposed methodology integrates the time-to-event outcome and patient-level potential prognostic longitudinal clinical outcomes. Leveraging on the fitted model, we conducted personalized prediction for each at-risk subject.
Results: The simulation studies established the superior predictive performance of the proposed method compared to benchmark model. Retrospective application in a randomized phase III oncology clinical trial underscored the model's accuracy, surpassing alternative benchmark models.
Conclusions: The proposed novel event prediction method advocates for the adoption of joint modeling as a robust strategy for event prediction. By harnessing the wealth of prognostic clinical markers, this approach improves prediction accuracy in clinical trials.

Event Prediction

Joint Modelling

Survival Analysis

Clinical Trials

Bayesian Analysis

Oncology Clinical Trials 

Section on Bayesian Statistical Science