|
Activity Number:
|
374
- Statistical Approaches for Evidence Integration and Considerations to Communication in Product Labeling
|
|
Type:
|
Topic-Contributed
|
|
Date/Time:
|
Thursday, August 12, 2021 : 12:00 PM to 1:50 PM
|
|
Sponsor:
|
Biopharmaceutical Section
|
|
Abstract #317468
|
|
|
Title:
|
Using Robust Bayesian Priors to Borrow Information from Historical Adult Trials for a Clinical Trial in Pediatric Multiple Sclerosis
|
|
Author(s):
|
Marius Thomas* and David Ohlssen and Heinz Schmidli and Dieter Haering
|
|
Companies:
|
Novartis and Novartis and Novartis and Novartis
|
|
Keywords:
|
Bayesian methods;
clinical trials;
pediatric extrapolation;
rare disease
|
|
Abstract:
|
Pediatric multiple sclerosis (MS) is a rare disease with a high unmet need for effective therapies. Due to the rarity of the diagnosis new trials need to be as efficient as possible to ensure successful completion. Using information from previous studies in adults can help to reduce the number of patients, however trials using historical information also need to remain robust to answer key scientific questions. We present a planned randomized controlled Phase 3 Bayesian trial design for evaluating two novel MS treatments, ofatumumab and siponimod, versus the approved treatment fingolimod in pediatric patients. The design makes use of data from historical studies in both adults and children to borrow information and reduce the required sample size for the new trial. Bayesian meta-analytic-predictive methods are used to incorporate the prior information in a principled way. Robustness to prior-data conflicts is increased by adding weakly informative components to the prior, which improve the operating characteristics of the design. The proposed trial design has been evaluated under US FDA’s Complex Innovative Designs pilot program and is planned to start recruiting in 2021.
|
Authors who are presenting talks have a * after their name.
