Evidence map›Paper›PMID 39277290›Full record

ReviewLancet (London, England)2024

Bayesian statistics for clinical research.

Ewan C Goligher, Anna Heath, Michael O Harhay

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Lancet (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
69citing papers in PubMed, 10 pooled it
–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

69 citing papers in PubMed, 10 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Pooled it
  9. Pooled it
  10. Pooled it
  11. Trial
  12. Trial
  13. Trial
  14. Trial
  15. Trial
  16. Trial
  17. Trial
  18. A Bayesian re-analysis of the DANFLU-1 trial.Human vaccines & immunotherapeutics · 2025
    Trial
  19. [Personalized sepsis therapy and modern trial designs].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2026
    Article
  20. Article

9 more citing papers are in PubMed but not listed here.

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

3 authors.

Ewan C GoligherInterdepartmental Division of Critical Care Medicine and Department of Physiology, University of Toronto, Toronto, ON, Canada; Department of Medicine, Division of Respirology, University Health Network, Toronto, ON, Canada; Toronto General Hospital Research Institute, Toronto, ON, Canada. Electronic address: ewan.goligher@utoronto.ca.
Anna HeathChild Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada; Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Michael O HarhayDepartment of Statistical Science (A Heath), University College London, London, UK; MRC Clinical Trials Unit, University College London, London, UK; Department of Biostatistics, Epidemiology, and Informatics and Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Funding

Advancing the design, analysis, and interpretation of acute respiratory distress syndrome trials using modern statistical toolsR01HL168202 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI Michael Oscar Harhay, Fan Li · 2023 to 2026
$2.9M
NHLBI NIH HHS R01 HL168202
6 · The paper itself

Abstract

Frequentist and Bayesian statistics represent two differing paradigms for the analysis of data. Frequentism became the dominant mode of statistical thinking in medical practice during the 20th century. The advent of modern computing has made Bayesian analysis increasingly accessible, enabling growing use of Bayesian methods in a range of disciplines, including medical research. Rather than conceiving of probability as the expected frequency of an event (purported to be measurable and objective), Bayesian thinking conceives of probability as a measure of strength of belief (an explicitly subjective concept). Bayesian analysis combines previous information (represented by a mathematical probability distribution, the prior) with information from the study (the likelihood function) to generate an updated probability distribution (the posterior) representing the information available for clinical decision making. Owing to its fundamentally different conception of probability, Bayesian statistics offers an intuitive, flexible, and informative approach that facilitates the design, analysis, and interpretation of clinical trials. In this Review, we provide a brief account of the philosophical and methodological differences between Bayesian and frequentist approaches and survey the use of Bayesian methods for the design and analysis of clinical research.

Indexed as

Bayes TheoremBiomedical ResearchClinical Trials as TopicData Interpretation, StatisticalResearch Design

Identifiers

PMID39277290
PMCPMC12051211

What OpenQuestion holds

Textmetadata
LicenceTDM
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.