Evidence map›Paper›PMID 36366347›Full record

ArticleVaccines2022

The Correlated Beta Dose Optimisation Approach: Optimal Vaccine Dosing Using Mathematical Modelling and Adaptive Trial Design.

John Benest, Sophie Rhodes, Thomas G Evans, Richard G White

Abstract read
In one paragraph

Article in Vaccines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

John BenestDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK.ORCID 0000-0003-0537-5437
Sophie RhodesDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK.
Thomas G EvansVaccitech Ltd., The Schrodinger Building, Heatley Road, The Oxford Science Park, Oxford OX4 4GE, UK.
Richard G WhiteDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK.

Funding

Defining drivers of TB transmission in the era of universal ART, and implications for finding the walking wellR01AI147321 · NIAID · LONDON SCH/HYGIENE & TROPICAL MEDICINE · PI GRANT, ALISON · 2019 to 2024
$2.3M
Bill & Melinda Gates Foundation OPP1084276Bill & Melinda Gates Foundation OPP1135288Biotechnology and Biological Sciences Research Council BB/M009513/1Economic and Social Research Council ES/P008011/1EDTCP RIA208D- 1150 2505BMedical Research Council CCF17-7779NIAID NIH HHS R01 AI147321NIH HHS 1R01AI147321-01Wellcome TrustWellcome Trust 218261/Z/19/ZWorld Health Organization 2020/985800-0
6 · The paper itself

Abstract

Mathematical modelling methods and adaptive trial design are likely to be effective for optimising vaccine dose but are not yet commonly used. This may be due to uncertainty with regard to the correct choice of parametric model for dose-efficacy or dose-toxicity. Non-parametric models have previously been suggested to be potentially useful in this situation. We propose a novel approach for locating optimal vaccine dose based on the non-parametric Continuous Correlated Beta Process model and adaptive trial design. We call this the 'Correlated Beta' or 'CoBe' dose optimisation approach. We evaluated the CoBe dose optimisation approach compared to other vaccine dose optimisation approaches using a simulation study. Despite using simpler assumptions than other modelling-based methods, we found that the CoBe dose optimisation approach was able to effectively locate the maximum efficacy dose for both single and prime/boost administration vaccines. The CoBe dose optimisation approach was also effective in finding a dose that maximises vaccine efficacy and minimises vaccine-related toxicity. Further, we found that these modelling methods can benefit from the inclusion of expert knowledge, which has been difficult for previous parametric modelling methods. This work further shows that using mathematical modelling and adaptive trial design is likely to be beneficial to locating optimal vaccine dose, ensuring maximum vaccine benefit and disease burden reduction, ultimately saving lives.

Indexed as

adaptive designclinical trialscontinual modellingdose responsedosingmodellingnon-parametric models

Identifiers

PMID36366347
PMCPMC9693615

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.