ArticleInternational journal of methods in psychiatric research2018
A Bayesian multivariate approach to estimating the prevalence of a superordinate category of disorders.
Article in International journal of methods in psychiatric research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
4 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Bayesian multivariate meta-analysis of prevalence data.Statistics in medicine · 2020Pooled it
- The Prevalence of Anxiety Disorders During Pregnancy and the Postpartum Period: A Multivariate Bayesian Meta-Analysis.The Journal of clinical psychiatry · 2019Pooled it
- Prevalence of physical abuse by caregivers toward infants aged 0-24 months: a systematic review and meta-analysis.EClinicalMedicine · 2026Article
- A Bayesian multivariate approach to estimating the prevalence of a superordinate category of disorders.International journal of methods in psychiatric research · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveEpidemiological research plays an important role in public health, facilitated by the meta-analytic aggregation of epidemiological trials into a single, more powerful estimate. This form of aggregation is complicated when estimating the prevalence of a superordinate category of disorders (e.g., "any anxiety disorder," "any cardiac disorder") because epidemiological studies rarely include all of the disorders selected to define the superordinate category. In this paper, we suggest that estimating the prevalence of a superordinate category based on studies with differing operationalization of that category (in the form of different disorders measured) is both common and ill-advised. Our objective is to provide a better approach.
methodsWe propose a multivariate method using individual disorder prevalences to produce a fully Bayesian estimate of the probability of having one or more of those disorders. We validate this approach using a recent case study and parameter recovery simulations.
resultsOur approach produced less biased and more reliable estimates than other common approaches, which were at times highly biased.
conclusionAlthough our approach entails additional effort (e.g., contacting authors for individual participant data), the improved accuracy of the prevalence estimates obtained is significant and therefore recommended.
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