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):
First Author:
Presenting Author:
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
Can this be considered for alternate subtype?
Yes
Are you interested in volunteering to serve as a session chair?
Yes
I have read and understand that JSM participants must abide by the Participant Guidelines.
Yes
I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.
I understand