A Bayesian multivariate meta-analysis of prevalence data

Lianne Siegel, Kyle Rudser, Siobhan Sutcliffe, Alayne Markland, Linda Brubaker, Sheila Gahagan, Ann E. Stapleton, Haitao Chu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

When conducting a meta-analysis involving prevalence data for an outcome with several subtypes, each of them is typically analyzed separately using a univariate meta-analysis model. Recently, multivariate meta-analysis models have been shown to correspond to a decrease in bias and variance for multiple correlated outcomes compared with univariate meta-analysis, when some studies only report a subset of the outcomes. In this article, we propose a novel Bayesian multivariate random effects model to account for the natural constraint that the prevalence of any given subtype cannot be larger than that of the overall prevalence. Extensive simulation studies show that this new model can reduce bias and variance when estimating subtype prevalences in the presence of missing data, compared with standard univariate and multivariate random effects models. The data from a rapid review on occupation and lower urinary tract symptoms by the Prevention of Lower Urinary Tract Symptoms Research Consortium are analyzed as a case study to estimate the prevalence of urinary incontinence and several incontinence subtypes among women in suspected high risk work environments.

Original languageEnglish (US)
Pages (from-to)3105-3119
Number of pages15
JournalStatistics in Medicine
Volume39
Issue number23
DOIs
StatePublished - Oct 15 2020

Bibliographical note

Funding Information:
The research is partially supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH) by cooperative agreements (grants U01DK106786, U01DK106853, U01DK106858, U01DK106898, U01DK106893, U01DK106827, U01DK106908, U01DK106892), and NIH National Library of Medicine R21LM012744 and R01LM012982, National Heart, Lung and Blood Institute T32HL129956, and the University of Minnesota Biostatistics Departmental Research Funds, and some additional funding from National Institute on Aging, NIH Office on Research in Women's Health and the NIH Office of Behavioral and Social Science. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Funding Information:
informationNational Heart, Lung, and Blood Institute, T32HL129956; National Institute of Diabetes and Digestive and Kidney Diseases, U01DK106786; U01DK106827; U01DK106853; U01DK106858; U01DK10689; U01DK106893; U01DK106898; U01DK106908; U.S. National Library of Medicine, R01LM012982; R21LM012744The research is partially supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH) by cooperative agreements (grants U01DK106786, U01DK106853, U01DK106858, U01DK106898, U01DK106893, U01DK106827, U01DK106908, U01DK106892), and NIH National Library of Medicine R21LM012744 and R01LM012982, National Heart, Lung and Blood Institute T32HL129956, and the University of Minnesota Biostatistics Departmental Research Funds, and some additional funding from National Institute on Aging, NIH Office on Research in Women's Health and the NIH Office of Behavioral and Social Science. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Publisher Copyright:
© 2020 John Wiley & Sons, Ltd.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Bayesian methods
  • meta-analysis
  • missing data
  • prevalence
  • sensitivity analysis
  • urinary incontinence

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural

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