A Bayesian Multivariate Meta-Analysis of Prevalence Data

• Activity Number: 662 - Methods for Meta-Analysis, and Longitudinal and Clustered Data
• Type: Contributed
• Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Lianne Siegel* and Kyle Rudser and Siobhan Sutcliffe and Alayne Markland and Linda Brubaker and Sheila Gahagan and Ann Stapleton and Haitao Chu
• Companies: University of Minnesota and University of Minnesota and Washington University School of Medicine and University of Alabama at Birmingham and Birmingham VA Medical Center and University of California San Diego and University of California San Diego and University of Washington and University of Minnesota
• Keywords: Bayesian methods; meta-analysis; prevalence; missing data; sensitivity analysis; urinary incontinence

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 to 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 to standard univariate and multivariate random effects models. The data from a rapid review on occupation and lower urinary tract symptoms are analyzed as a case study to estimate the prevalence of urinary incontinence and several incontinence subtypes among women in high risk work environments.