Print  Close

Bayesian Hierarchical Borrowing for Platform Trials with Non-Linear Longitudinal Outcomes

Abstract Number:

1994 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Xiaoting Xing (1), Thaddeus Tarpey (1)

Institutions:

(1) NYU Grossman School of Medicine, NY

Co-Author:

Thaddeus Tarpey  
NYU Grossman School of Medicine

First Author:

Xiaoting Xing  
NYU Grossman School of Medicine

Presenting Author:

Xiaoting Xing  
NYU Grossman School of Medicine

Abstract Text:

Platform trials are multi-arm designs that simultaneously evaluate multiple treatments for a single disease under a shared protocol, benefiting from control data borrowing to improve statistical efficiency. Longitudinal outcomes can provide more precise estimates and increase statistical power for platform analysis. However, relatively few studies have addressed borrowing approaches for longitudinal outcomes in platform trials. In pain and depression studies, outcome trajectories are often nonlinear. To address these issues, we extend Bayesian hierarchical borrowing methods (BHM) to longitudinal endpoints, incorporating nonlinear features within a causal inference framework. We investigate the performance and benefits of various pooling methods: simple pooling, eligible pooling, BHM, and incorporating flexible and adaptable patient-level weights in BHM, in comparison to no borrowing methods. The BHM framework introduces hierarchical structures to balance the extent of borrowing based on data similarity across regimens, optimizing inference while maintaining Type I error control. Simulation results will be presented to compare different borrowing approaches.

Keywords:

Bayesian Hierarchical Borrowing| platform trials|Longitudinal|non-linear | |

Sponsors:

Biometrics Section

Tracks:

Longitudinal/Correlated Data

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 3, 2025. The registration fee is non-refundable.

I understand