Evidence map›Paper›PMID 37081374›Full record

ArticleTherapeutic innovation & regulatory science2023

A Tutorial on Modern Bayesian Methods in Clinical Trials.

Natalia Muehlemann, Tianjian Zhou, Rajat Mukherjee, Munshi Imran Hossain, Satrajit Roychoudhury, Estelle Russek-Cohen

Abstract read
In one paragraph

Article in Therapeutic innovation & regulatory science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 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

22 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

6 authors.

Natalia MuehlemannCytel, Cambridge, MA, USA. natalia.muhlemann@cytel.com.ORCID 0000-0001-5167-904X
Tianjian ZhouDepartment of Statistics, Colorado State University, Fort Collins, CO, USA.
Rajat MukherjeeAlira Health, Barcelona, Spain.
Munshi Imran HossainCytel, Cambridge, MA, USA.
Satrajit RoychoudhuryPfizer Inc., New York, NY, USA.
Estelle Russek-CohenERCStat, Silver Spring, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical trials continue to be the gold standard for evaluating new medical technologies. New advancements in modern computation power have led to increasing interest in Bayesian methods. Despite the multiple benefits of Bayesian approaches, application to clinical trials has been limited. Based on insights from the survey of clinical researchers in drug development conducted by the Drug Information Association Bayesian Scientific Working Group (DIA BSWG), insufficient knowledge of Bayesian approaches was ranked as the most important perceived barrier to implementing Bayesian methods. Results of the same survey indicate that clinical researchers may find the interpretation of results from a Bayesian analysis to be more useful than conventional interpretations. In this article, we illustrate key concepts tied to Bayesian methods, starting with familiar concepts widely used in clinical practice before advancing in complexity, and use practical illustrations from clinical development.

Indexed as

Drug DevelopmentBayes TheoremClinical Trials as TopicBayesian methodsBayesian statisticsClinical developmentClinical trialsDrug development

Identifiers

PMID37081374
PMCPMC10117244

What OpenQuestion holds

Textmetadata
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.