Abstract

Meta-analysis provides an integrated analysis and summary of the effects observed in \(k\) independent studies. The conventional analysis proceeds by first calculating a study-specific effect estimate, and then provides further analysis on the basis of the available \(k\) independent effect estimates associated with their uncertainty measures. Here we consider a setting where counts of events are available from \(k\) independent studies for a treatment and a control group. We suggest to model this situation with a study-specific Poisson regression model, and allow the study-specific parameters of the Poisson model to arise from a nonparametric mixture model. This approach then allows the estimation of the heterogeneity variance of the effect measure of interest in a nonparametric manner. A case study is used to illustrate the methodology throughout the paper.

中文翻译：

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罕见事件荟萃分析中效应异质性的非参数估计：双变量，离散混合物模型

摘要

荟萃分析提供了对（k）独立研究中观察到的影响的综合分析和总结。常规分析通过首先计算特定于研究的效果估计进行，然后基于与不确定性度量相关的可用\（k \）独立效果估计提供进一步的分析。在这里，我们考虑一种设置，其中可以从\（k \）获得事件计数对治疗和对照组进行独立研究。我们建议使用研究专用的Poisson回归模型对这种情况进行建模，并允许Poisson模型的研究专用参数来自非参数混合模型。然后，该方法允许以非参数方式估计感兴趣的效果度量的异质性方差。本文以案例研究为例来说明该方法。