Trial reportTransfusion2020
Elicitation of prior probability distributions for a proposed Bayesian randomized clinical trial of whole blood for trauma resuscitation.
Trial report in Transfusion, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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.
Who cites it
10 citing papers in PubMed.
- The UK resuscitative endovascular balloon occlusion of the aorta in trauma patients with life-threatening torso haemorrhage: the (UK-REBOA) multicentre RCT.Health technology assessment (Winchester, England) · 2024Trial
- Article
- INCEPT: The Intensive Care Platform Trial-Design and protocol.Acta anaesthesiologica Scandinavica · 2025Article
- Expert Judgment Supporting a Bayesian Network to Model the Survival of Pancreatic Cancer Patients.Cancers · 2025Article
- Digital twin mathematical models suggest individualized hemorrhagic shock resuscitation strategies.Communications medicine · 2024Article
- Bayesian adaptive design for pediatric clinical trials incorporating a community of prior beliefs.BMC medical research methodology · 2022Article
- Article
- Effectiveness of initial methotrexate-based treatment approaches in early rheumatoid arthritis: an elicitation of rheumatologists' beliefs.Rheumatology (Oxford, England) · 2021Article
- Bayesian Statistics for Surgical Decision Making.Surgical infections · 2021Review
- Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review.International journal of environmental research and public health · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundWhole blood trauma resuscitation is conceptually appealing and increasingly used but lacks evidence. A randomized controlled trial is needed but challenging to design. A Bayesian approach might be more efficient and more interpretable than a conventional frequentist design. We report the results on an elicitation meeting to create prior probability distributions to help develop such a trial.
methodsIn-person expert elicitation meeting, based on Sheffield Elicitation Framework methodology. We used an interactive graphical tool to elicit the quantities of interest (24-hour mortality and certainty required). Two rounds were conducted, with an intervening discussion of deidentified responses. Individual responses were aggregated into probability distributions.
resultsFifteen experts participated. The pooled belief was that the median 24-hour mortality of trauma patients with hemorrhagic shock treated with component therapy (the current standard of care) was 19% (95% credible interval [CrI], 6%-45%), and the median 24-hour mortality of those treated with whole blood, 16% (95% CrI, 5%-39%). The pooled prior distribution for the relative risk had a median of 0.84 (95% CrI, 0.26-3.1), indicating that the expert group had a 64% prior belief that whole blood decreases 24-hour mortality compared to component therapy.
conclusionsExperts had moderately strong beliefs that whole blood reduces the 24-hour mortality of trauma patients with hemorrhagic shock. These data will assist with the design and planning of a Bayesian trial of whole blood resuscitation, which will help to answer a key question in contemporary transfusion practice.
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Registered trials
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.