Evidence map›Paper›PMID 42359272›Full record

ArticleCHEST critical care2026

Barriers to Adopting Evidence From Bayesian Adaptive Clinical Trials in Critical Care: A Randomized Experiment With US Intensivists.

Brian E Malley, Joel M Levin, Jeremy M Kahn, Leigh A Bukowski, David T Huang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in CHEST critical care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Brian E MalleyCenter for Research, Investigation, and Systems Modeling of Acute Illness (B. E. M., J. M. K., L. A. B., and D. T. H.), Department of Critical Care Medicine, the Joseph M. Katz Graduate School of Business (J. M. L.), University of Pittsburgh, Pittsburgh, PA; the Department of Emergency Medicine (B. E. M.), Maine Medical Center, Portland, ME; and the Rady School of Management (J. M. L.), University of California, San Diego, San Diego, CA.
Joel M LevinCenter for Research, Investigation, and Systems Modeling of Acute Illness (B. E. M., J. M. K., L. A. B., and D. T. H.), Department of Critical Care Medicine, the Joseph M. Katz Graduate School of Business (J. M. L.), University of Pittsburgh, Pittsburgh, PA; the Department of Emergency Medicine (B. E. M.), Maine Medical Center, Portland, ME; and the Rady School of Management (J. M. L.), University of California, San Diego, San Diego, CA.
Jeremy M KahnCenter for Research, Investigation, and Systems Modeling of Acute Illness (B. E. M., J. M. K., L. A. B., and D. T. H.), Department of Critical Care Medicine, the Joseph M. Katz Graduate School of Business (J. M. L.), University of Pittsburgh, Pittsburgh, PA; the Department of Emergency Medicine (B. E. M.), Maine Medical Center, Portland, ME; and the Rady School of Management (J. M. L.), University of California, San Diego, San Diego, CA.
Leigh A BukowskiCenter for Research, Investigation, and Systems Modeling of Acute Illness (B. E. M., J. M. K., L. A. B., and D. T. H.), Department of Critical Care Medicine, the Joseph M. Katz Graduate School of Business (J. M. L.), University of Pittsburgh, Pittsburgh, PA; the Department of Emergency Medicine (B. E. M.), Maine Medical Center, Portland, ME; and the Rady School of Management (J. M. L.), University of California, San Diego, San Diego, CA.
David T HuangCenter for Research, Investigation, and Systems Modeling of Acute Illness (B. E. M., J. M. K., L. A. B., and D. T. H.), Department of Critical Care Medicine, the Joseph M. Katz Graduate School of Business (J. M. L.), University of Pittsburgh, Pittsburgh, PA; the Department of Emergency Medicine (B. E. M.), Maine Medical Center, Portland, ME; and the Rady School of Management (J. M. L.), University of California, San Diego, San Diego, CA.

Funding

Organizational strategies for improving evidence-uptake in intensive careR35HL144804 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI KAHN, JEREMY M · 2019 to 2025
$6.6M
Multidisciplinary Training in Critical Care Outcomes ResearchT32HL007820 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Timothy D Girard, Jeremy M Kahn · 1996 to 2026
$6.4M
NHLBI NIH HHS R35 HL144804NHLBI NIH HHS T32 HL007820
6 · The paper itself

Abstract

backgroundRandomized clinical trials are a mainstay of medical research, but have drawbacks including time and cost. Bayesian adaptive trials can improve the speed and efficiency of clinical trials and are an increasingly common trial design in critical care. However, the complexity of Bayesian adaptive trials may prevent clinicians from easily interpreting their results. RESEARCH QUESTION: Do intensivist physicians perceive, understand, and accept results from Bayesian adaptive clinical trials differently than results from traditional frequentist trials when presented with otherwise identical data? STUDY DESIGN AND

methodsWe surveyed US intensivists from March through April 2022 about their perceptions of Bayesian adaptive trials. Within the survey, participants were randomized to read an abstract for a hypothetical new sepsis drug trial that used either Bayesian adaptive methods or traditional frequentist methods, with both abstracts based on the same simulated trial data. Participants then were asked about perceived understanding, validity, and general acceptance of the trial's methods and results. Survey responses were compared between experimental groups using Mann-Whitney

resultsWe received complete results from 273 of 592 eligible physicians (response rate, 46.1%). Participants in the Bayesian group reported lesser understanding of the methods (mean rating in frequentist vs Bayesian groups, 3.18 vs 2.85;

interpretationOur results show that despite lower perceived understanding of Bayesian trials and skepticism about elements of their validity, no substantive differences were found in intensivist physician acceptance of Bayesian trial results compared with frequentist trial results. CHEST Critical Care 2026; 4(2):100264.

Indexed as

Bayesian statisticsclinical trialscritical carefrequentist statisticsimplementation scienceintensive caresepsissurvey methods

Identifiers

PMID42359272
PMCPMC13293486

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.