Abstract

Multiple category prevalence represents the prevalence of the specific disease with different k-category (k > 2) statuses, such as mild, moderate and severe. This study proposed the Bayesian method for the meta-analysis of studies with multiple category prevalence. The Dirichlet-multinomial model was used to obtain the Bayesian approach. In this way, both the opportunity to consider the preinformation regarding the prevalences and to obtain an effective estimation regardless of the value of the prevalences (around 0.5 or close to 0-1) was possible. The proposed method was compared with the frequentist method based on simulation and Barendregt et al. (2013 Barendregt, J. J., S. A. Doi, Y. Y. Lee, R. E. Norman, and T. Vos. 2013. Meta-analysis of prevalence. Journal of Epidemiology and Community Health 67 (11):974–8. doi:10.1136/jech-2013-203104.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) data. It was demonstrated that without requiring any transformation, the Bayesian method resulted in more consistency; smaller relative errors and mean squared errors, powerful accepted probability estimators, especially for small total sample sizes; and prevalences close to 0-1.

摘要

多类别患病率代表具有不同k类别（k  > 2）状态（例如轻度，中度和重度）的特定疾病的患病率。这项研究提出了贝叶斯方法，用于多类别患病率研究的荟萃分析。使用Dirichlet多项式模型获得贝叶斯方法。以此方式，既有机会考虑有关患病率的预先信息并获得有效估计，而与患病率的值无关（约0.5或接近0-1）都是可能的。将所提出的方法与基于仿真和Barendregt等人的频率论方法进行了比较。（2013年 Barendregt，JJ，SA Doi，YY Lee，RE Norman和T. Vos。2013年。患病率的荟萃分析。流行病学与社区卫生杂志67（11）：974 – 8。doi：10.1136 / jech-2013-203104。[Crossref]，[PubMed]，[Web of Science®]， [Google Scholar]） 数据。事实证明，不需要任何变换，贝叶斯方法可以提高一致性。较小的相对误差和均方误差，强大的接受概率估计器，尤其是对于较小的总样本量而言；患病率接近0-1。