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.

中文翻译：

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患病率数据的贝叶斯多元荟萃分析。

当对涉及几种亚型的结果的患病率数据进行荟萃分析时，通常使用单变量荟萃分析模型分别分析每种亚型。最近，当一些研究仅报告一部分结果时，多元荟萃分析模型显示与单因素荟萃分析相比，多重相关结果的偏倚和方差减少。在本文中，我们提出了一种新颖的贝叶斯多元随机效应模型来解决自然约束，即任何给定亚型的患病率都不能大于整体患病率。大量的仿真研究表明，与标准单变量和多变量随机效应模型相比，在缺少数据的情况下估计亚型患病率时，该新模型可以减少偏差和方差。