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

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

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
1471-2288
Ewings, Sean
326656df-c0f0-44a1-b64f-8fe9578ca18a
Saunders, Geoffrey
003d2b6f-fbfd-4247-911c-8b57a83f1fd7
Jaki, Thomas
9f90d14d-1b72-4192-b3c7-2ad87d28e7d0
Mozgunov, Pavel
90afb1e5-3481-4e98-9e04-3128554b188e
Ewings, Sean
326656df-c0f0-44a1-b64f-8fe9578ca18a
Saunders, Geoffrey
003d2b6f-fbfd-4247-911c-8b57a83f1fd7
Jaki, Thomas
9f90d14d-1b72-4192-b3c7-2ad87d28e7d0
Mozgunov, Pavel
90afb1e5-3481-4e98-9e04-3128554b188e

Ewings, Sean, Saunders, Geoffrey, Jaki, Thomas and Mozgunov, Pavel (2022) Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic. BMC Medical Research Methodology, 22 (1), [25]. (doi:10.1186/s12874-022-01512-0).

Record type: Article

Abstract

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.

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Accepted/In Press date: 6 January 2022
Published date: 20 January 2022
Additional Information: Funding This report is independent research supported by the National Institute for Health Research (NIHR Advanced Fellowship, Dr. Pavel Mozgunov, NIHR300576; and Professor Thomas Jaki’s Senior Research Fellowship, NIHR-SRF-2015-08-001), the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014) and the Cancer Research UK and NIHR-funded Southampton Clinical Trials Unit (UKCRC ID 37). The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, CRUK or the Department of Health and Social Care. T Jaki and P Mozgunov received funding from UK Medical Research Council (MC_UU_00002/14). The Molnupiravir study received funding support from Ridgeback Biotherapeutics, USA. Ridgeback played no role in the design, data collection, analysis, or interpretation of the study. The AGILE platform trial is funded in part by the Wellcome Trust (Grant reference: 221590/Z/20/Z) and the UK Medical Research Council (Grant reference: MR/V028391/1).
Keywords: Adaptive design, Bayesian, Dose escalation, Phase I

Identifiers

Local EPrints ID: 454652
URI: http://eprints.soton.ac.uk/id/eprint/454652
ISSN: 1471-2288
PURE UUID: a08613f3-11e1-4c7e-bf3b-16a6520eda3a
ORCID for Sean Ewings: ORCID iD orcid.org/0000-0001-7214-4917

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Date deposited: 17 Feb 2022 17:53
Last modified: 28 Apr 2022 02:02

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Contributors

Author: Sean Ewings ORCID iD
Author: Geoffrey Saunders
Author: Thomas Jaki
Author: Pavel Mozgunov

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