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

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.

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Publication Details

Journal
PLOS mental health.
Published
2025-02-26
DOI
https://doi.org/10.1371/journal.pmen.0000253
Citations
2
Primary Topic
Mental Health Research Topics
Type
article
Field-Weighted Citation Impact
4.24

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article

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, Sameer Parpia, Glenda Babe, Lehana Thabane et al.
2 citations
PLOS mental health.
Mental Health Research Topics
4.24
article

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, Sameer Parpia, Glenda Babe, Lehana Thabane, Richard Perez, Saifur Rahman Chowdhury, Jordan Edwards, Yuanyuan Wang, Tea Rosic, Jhalok Ronjan Talukdar, Claire de Oliveira, Zainab Samaan
article en
2 citations

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.

PLOS mental health.Vol. 2(2)
Centre for Addiction and Mental Health (CA), University of Ottawa (CA), University of Toronto (CA), Queen's University (CA), University of Johannesburg (ZA), Children's Hospital of Eastern Ontario (CA), Impact (CA), St. Joseph’s Healthcare Hamilton (CA), McMaster University (CA)
McMaster University, Institute of Neurosciences, Mental Health and Addiction
Openalex Percentile: Top 8%
Mental Health Research Topics
4.24
DOIOpen Access PDFOpenAlex
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