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, Zainab Samaan and Lehana Thabane

PLOS Mental Health, 2025, vol. 2, issue 2, 1-25

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.Author summary: Mental health conditions, like anxiety and depression, are common and can greatly impact people’s lives. Knowing how many people are affected is important for planning healthcare services and improving treatment. However, combining information from different sources, such as health surveys and medical records, may be challenging with traditional methods.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pmen00:0000253

DOI: 10.1371/journal.pmen.0000253

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