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Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic.

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Ewings, Sean 
Saunders, Geoff 
Mozgunov, Pavel 


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.



Adaptive design, Bayesian, Dose escalation, Phase I, Bayes Theorem, COVID-19, Dose-Response Relationship, Drug, Humans, Maximum Tolerated Dose, Pandemics, Research Design, SARS-CoV-2

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BMC Med Res Methodol

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Springer Science and Business Media LLC
Medical Research Council (MC_UU_00002/14)
NIHR Academy (SRF-2015-08-001)
UK Research and Innovation (MR/V028391/1)
National Institute for Health and Care Research (IS-BRC-1215-20014)
Wellcome Trust (221590/Z/20/Z)