Evidence map›Paper›PMID 33196672›Full record

ArticlePLoS neglected tropical diseases2020

A Bayesian phase 2 model based adaptive design to optimise antivenom dosing: Application to a dose-finding trial for a novel Russell's viper antivenom in Myanmar.

James A Watson, Thomas Lamb, Jane Holmes, David A Warrell, Khin Thida Thwin, Zaw Lynn Aung, Min Zaw Oo, Myat Thet Nwe, Frank Smithuis, Elizabeth A Ashley

Open access · goldAbstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Managing snakebite.BMJ (Clinical research ed.) · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 5 institutions in 4 countries.

James A WatsonMahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.ORCID 0000-0001-5524-0325
Thomas LambCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.ORCID 0000-0001-6102-2981
Jane HolmesCentre for Statistics in Medicine, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.ORCID 0000-0001-7123-0004
David A WarrellCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.ORCID 0000-0001-7792-1615
Khin Thida ThwinUniversity of Medicine 1, Yangon, Myanmar.
Zaw Lynn AungUniversity of Medicine 1, Yangon, Myanmar.
Min Zaw OoUniversity of Medicine 2, Yangon, Myanmar.
Myat Thet NweMyanmar-Oxford Clinical Research Unit, Yangon, Myanmar.ORCID 0000-0001-7136-8636
Frank SmithuisCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
Elizabeth A AshleyCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-7620-4822
University of Oxford · GBUniversity Of Medicine 1 Yangon · MMMahidol University · THMyanmar Oxford Clinical Research Unit · MMUniversity of Medicine 2 Yangon · MM

Funding

Cancer Research UK 27294Wellcome Trust
6 · The paper itself

Abstract

For most antivenoms there is little information from clinical studies to infer the relationship between dose and efficacy or dose and toxicity. Antivenom dose-finding studies usually recruit too few patients (e.g. fewer than 20) relative to clinically significant event rates (e.g. 5%). Model based adaptive dose-finding studies make efficient use of accrued patient data by using information across dosing levels, and converge rapidly to the contextually defined 'optimal dose'. Adequate sample sizes for adaptive dose-finding trials can be determined by simulation. We propose a model based, Bayesian phase 2 type, adaptive clinical trial design for the characterisation of optimal initial antivenom doses in contexts where both efficacy and toxicity are measured as binary endpoints. This design is illustrated in the context of dose-finding for Daboia siamensis (Eastern Russell's viper) envenoming in Myanmar. The design formalises the optimal initial dose of antivenom as the dose closest to that giving a pre-specified desired efficacy, but resulting in less than a pre-specified maximum toxicity. For Daboia siamensis envenoming, efficacy is defined as the restoration of blood coagulability within six hours, and toxicity is defined as anaphylaxis. Comprehensive simulation studies compared the expected behaviour of the model based design to a simpler rule based design (a modified '3+3' design). The model based design can identify an optimal dose after fewer patients relative to the rule based design. Open source code for the simulations is made available in order to determine adequate sample sizes for future adaptive snakebite trials. Antivenom dose-finding trials would benefit from using standard model based adaptive designs. Dose-finding trials where rare events (e.g. 5% occurrence) are of clinical importance necessitate larger sample sizes than current practice. We will apply the model based design to determine a safe and efficacious dose for a novel lyophilised antivenom to treat Daboia siamensis envenoming in Myanmar.

Indexed as

Adaptive Clinical Trials as TopicAnaphylaxisAnimalsAntiveninsBayes TheoremBlood CoagulationClinical Trials, Phase II as TopicComputer SimulationDaboiaDose-Response Relationship, DrugHumansModels, StatisticalMyanmarSnake BitesViper VenomsAntiveninsViper Venoms

Identifiers

PMID33196672
PMCPMC7704047
OpenAlexW3005403460

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.