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A Bayesian multivariate approach to estimating the prevalence of a superordinate category of disorders
Author(s) -
Fawcett Jonathan M.,
Fairbrother Nichole,
Fawcett Emily J.,
White Ian R.
Publication year - 2018
Publication title -
international journal of methods in psychiatric research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.275
H-Index - 73
eISSN - 1557-0657
pISSN - 1049-8931
DOI - 10.1002/mpr.1742
Subject(s) - superordinate goals , multivariate statistics , epidemiology , nosology , operationalization , bayesian probability , multivariate analysis , clinical psychology , medicine , anxiety , psychology , statistics , psychiatry , mathematics , social psychology , pathology , philosophy , epistemology
Objective Epidemiological 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. Methods We 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. Results Our approach produced less biased and more reliable estimates than other common approaches, which were at times highly biased. Conclusion Although 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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