A Bayesian model with seasonal effects for predicting accrual in clinical trials: Application to HOBIT and BOOST-3 trials for severe traumatic brain injury
Incorporating seasonal effects into accrual modeling can enhance prediction accuracy, particularly in larger trials with high enrollment, and supports more accurate trial forecasting and resource allocation.
Authors
- Byron J. Gajewski (ORCID: https://orcid.org/0000-0001-7589-8247)
- Frederick K. Korley (ORCID: https://orcid.org/0000-0003-4920-8278)
- Mohammod Mahmudur Rahman (ORCID: https://orcid.org/0009-0000-4659-9299)
- Robert Silbergleit (ORCID: https://orcid.org/0000-0003-4101-2430)
- Jonathan Beall (ORCID: https://orcid.org/0000-0003-2135-2606)
- Valerie Stevenson
- William G. Barsan (ORCID: https://orcid.org/0000-0002-6019-2062)
- M Saif
- Renee Martin
- Gaylan L. Rockswold
Institutions
- University of Minnesota (US)
- Medical University of South Carolina (US)
- University of Michigan (US)
- Hennepin County Medical Center (US)
- University of Kansas Medical Center (US)
Publication Details
- Journal
- Contemporary Clinical Trials Communications
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1016/j.conctc.2025.101586
- Primary Topic
- Traumatic Brain Injury and Neurovascular Disturbances
- Type
- article
- Field-Weighted Citation Impact
- 0.00