Evidence map›Paper›PMID 41661841›Full record

ArticlePLOS mental health2025

A tutorial on the what, why, and how of Bayesian analysis: Estimating mood and anxiety disorder prevalence using a Canadian data linkage study.

Myanca Rodrigues, Jordan Edwards, Tea Rosic, Yanchen Wang, Jhalok Ronjan Talukdar, Saifur R Chowdhury, Sameer Parpia, Glenda Babe, Claire de Oliveira, Richard Perez and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PLOS mental health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

12 authors.

Myanca RodriguesDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.ORCID https://orcid.org/0000-0001-7953-773X
Jordan EdwardsOfford Centre for Child Studies, McMaster University, Hamilton, Canada.
Tea RosicDepartment of Psychiatry, University of Ottawa, Ottawa, Canada.ORCID https://orcid.org/0000-0001-7406-4056
Yanchen WangDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.
Jhalok Ronjan TalukdarDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.ORCID https://orcid.org/0000-0001-9739-5632
Saifur R ChowdhuryDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.
Sameer ParpiaDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.
Glenda BabeThe Centre for Addiction and Mental Health, Toronto, Canada.
Claire de OliveiraThe Centre for Addiction and Mental Health, Toronto, Canada.ORCID https://orcid.org/0000-0003-3961-6008
Richard PerezDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.
Zainab SamaanDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.
Lehana ThabaneDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.ORCID https://orcid.org/0000-0003-0355-9734

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bayesian analyses offer a robust framework for integrating data from multiple sources to better inform population-level estimates of disease prevalence. This methodological approach is particularly suited to instances where data from observational studies is linked to administrative health records, with the capacity to advance our understanding of psychiatric disorders. The objective of our paper was to provide an introductory overview and tutorial on Bayesian analysis for primary observational studies in mental health research. We provided: (i) an overview of Bayesian statistics, (ii) the utility of Bayesian methods for psychiatric epidemiology, (iii) a tutorial example of a Bayesian approach to estimating the prevalence of mood and/or anxiety disorders in observational research, and (iv) suggestions for reporting Bayesian analyses in health research.

Identifiers

PMID41661841
PMCPMC12798518

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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.