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.
In this study, we demonstrate how Bayesian analysis, a statistical approach, may address these challenges. Using a real-world example from a study on people with opioid use disorder, we demonstrate how this method provides more accurate estimates of how common mental health conditions are. Our results highlight that Bayesian analysis effectively integrates data from different sources to produce reliable prevalence estimates.
This tutorial is the first step-by-step guide to help researchers apply Bayesian analysis in mental health research. By making this method more accessible, we aim to improve how mental illness is measured. This can ultimately help healthcare providers make better decisions about resource allocation and care for people with mental health needs.