Premium
A Bayesian multivariate meta‐analysis of prevalence data
Author(s) -
Siegel Lianne,
Rudser Kyle,
Sutcliffe Siobhan,
Markland Alayne,
Brubaker Linda,
Gahagan Sheila,
Stapleton Ann E.,
Chu Haitao
Publication year - 2020
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8593
Subject(s) - univariate , multivariate statistics , meta analysis , multivariate analysis , random effects model , statistics , missing data , bayesian probability , medicine , mathematics
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.