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Event Prediction with Prognostic Clinical Markers by Joint Modelling

Abstract Number:

1867 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Rui Kang (1), Xuehan Ren (2), Hao Wang (3), Lanjia Lin (4)

Institutions:

(1) University of Pittsburgh, N/A, (2) Gilead Sciences, N/A, (3) Kite, N/A, (4) N/A, N/A

Co-Author(s):

Xuehan Ren  
Gilead Sciences
Hao Wang  
Kite
Lanjia Lin  
N/A

First Author:

Rui Kang  
University of Pittsburgh

Presenting Author:

Rui Kang  
University of Pittsburgh

Abstract Text:

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.

Keywords:

Event Prediction|Joint Modelling|Survival Analysis|Clinical Trials|Bayesian Analysis|Oncology Clinical Trials

Sponsors:

Section on Bayesian Statistical Science

Tracks:

Applications in Life Sciences and Medicine

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