An efficient single-arm Bayesian adaptive trial algorithm to evaluate de-intensified oncologic treatment
Yuan Zhong, Zeynep Baskurt, Mahmood Aminilari, Jennifer Seelisch, Lindsay A Renfro, Sharon M Castellino, Wei Xu, David Hodgson · 2025
Read An efficient single-arm Bayesian adaptive trial algorithm to evaluate de-intensified oncologic treatment by Yuan Zhong, Zeynep Baskurt, Mahmood Aminilari, Jennifer Seelisch, Lindsay A Renfro, Sharon M Castellino, Wei Xu, David Hodgson (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12801998.txt.
Description
Yuan Zhong and seven co-authors present a trial design for testing gentler cancer treatment, in a 2025 paper. Lower-dose treatment can spare patients side effects and protect quality of life. The catch is that such trials usually target cancers with low relapse rates, and rare cancers need impractically long studies to gather enough events with a randomized design.
Their answer is a single-arm Bayesian adaptive method for survival data. It borrows knowledge from earlier studies to form a prior and a historical control group. That support sharpens estimates for design, sample size, and final decisions. The team built an R package, BayesAT, that handles staged interim looks at the data. Simulations and sensitivity checks validated the approach. It was also applied to a childhood Hodgkin lymphoma trial.
Catalog record
| CREATOR | Yuan Zhong, Zeynep Baskurt, Mahmood Aminilari, Jennifer Seelisch, Lindsay A Renfro, Sharon M Castellino, Wei Xu, David Hodgson |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 43 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |