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Meta-Analysis
. 2020 Oct 15;39(23):3105-3119.
doi: 10.1002/sim.8593. Epub 2020 Jun 8.

A Bayesian multivariate meta-analysis of prevalence data

Lianne Siegel  1 , Kyle Rudser  1 , Siobhan Sutcliffe  2 , Alayne Markland  3   4 , Linda Brubaker  5 , Sheila Gahagan  6 , Ann E Stapleton  7 , Haitao Chu  1
Affiliations
Meta-Analysis

A Bayesian multivariate meta-analysis of prevalence data

Lianne Siegel et al. Stat Med. .

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.

Keywords: Bayesian methods; meta-analysis; missing data; prevalence; sensitivity analysis; urinary incontinence.

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Figures

FIGURE 1
FIGURE 1. Forest Plot of Study Level Estimates
Posterior mean and 95% credible interval of marginal and study level prevalences for each of the three outcomes across 26 studies
FIGURE 2
FIGURE 2. Bivariate Density Plots for Predicted Prevalences in New Study
(a) Overall and SUI posterior predicted prevalences for new study based on original multivariate model results, (b) Overall and UUI with original multivariate model, (c) Overall and SUI with new parameterization, (d) Overall and UUI with new parameterization
FIGURE 3
FIGURE 3. Sensitivity Analysis
Posterior mean and 95% credible intervals for UI, SUI, and UUI marginal prevalence across values of α2
See this image and copyright information in PMC

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