Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic.
BMC Med Res Methodol
Springer Science and Business Media LLC
MetadataShow full item record
Ewings, S., Saunders, G., Jaki, T., & Mozgunov, P. (2022). Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic.. BMC Med Res Methodol, 22 (1) https://doi.org/10.1186/s12874-022-01512-0
BACKGROUND: Modern designs for dose-finding studies (e.g., model-based designs such as continual reassessment method) have been shown to substantially improve the ability to determine a suitable dose for efficacy testing when compared to traditional designs such as the 3 + 3 design. However, implementing such designs requires time and specialist knowledge. METHODS: We present a practical approach to developing a model-based design to help support uptake of these methods; in particular, we lay out how to derive the necessary parameters and who should input, and when, to these decisions. Designing a model-based, dose-finding trial is demonstrated using a treatment within the AGILE platform trial, a phase I/II adaptive design for novel COVID-19 treatments. RESULTS: We present discussion of the practical delivery of AGILE, covering what information was found to support principled decision making by the Safety Review Committee, and what could be contained within a statistical analysis plan. We also discuss additional challenges we encountered in the study and discuss more generally what (unplanned) adaptations may be acceptable (or not) in studies using model-based designs. CONCLUSIONS: This example demonstrates both how to design and deliver an adaptive dose-finding trial in order to support uptake of these methods.
Research, Bayesian, Phase I, Adaptive design, Dose escalation
External DOI: https://doi.org/10.1186/s12874-022-01512-0
This record's URL: https://www.repository.cam.ac.uk/handle/1810/333195