Bayesian Optimal Phase II design with optimised stopping boundaries and response-adaptive randomisation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912716812189696 |
|---|---|
| author | Fitchett, Connor Mukherjee, Ayon Villar, Sofía S. Robertson, David S. |
| author_facet | Fitchett, Connor Mukherjee, Ayon Villar, Sofía S. Robertson, David S. |
| contents | The Bayesian Optimal Phase II (BOP2) framework is a flexible trial design that can naturally facilitate complex adaptations due to its Bayesian setting. BOP2 uses equal randomisation and equally placed interim analyses in its design, but it is unclear whether these give the best operating characteristics. By incorporating Bayesian Response-Adaptive Randomisation (BRAR) and optimal interim analysis placement, we show that allocation to the best treatment and expected sample size can be improved with minimal impact on power. We discuss recommendations on implementing these adaptations, using simulation-based evidence, to give practical advice to practitioners. Reproducible code for the simulations is freely provided. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14714 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian Optimal Phase II design with optimised stopping boundaries and response-adaptive randomisation Fitchett, Connor Mukherjee, Ayon Villar, Sofía S. Robertson, David S. Applications The Bayesian Optimal Phase II (BOP2) framework is a flexible trial design that can naturally facilitate complex adaptations due to its Bayesian setting. BOP2 uses equal randomisation and equally placed interim analyses in its design, but it is unclear whether these give the best operating characteristics. By incorporating Bayesian Response-Adaptive Randomisation (BRAR) and optimal interim analysis placement, we show that allocation to the best treatment and expected sample size can be improved with minimal impact on power. We discuss recommendations on implementing these adaptations, using simulation-based evidence, to give practical advice to practitioners. Reproducible code for the simulations is freely provided. |
| title | Bayesian Optimal Phase II design with optimised stopping boundaries and response-adaptive randomisation |
| topic | Applications |
| url | https://arxiv.org/abs/2511.14714 |