使用贝叶斯-二项模型的罕见事件元分析。

IF 5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Katrin Jansen, Heinz Holling
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引用次数: 0

摘要

在罕见事件的荟萃分析中,获得合并效应的可靠估计可能具有挑战性,尤其是当荟萃分析基于少量研究时。最近的模拟研究表明,在这种情况下,β二项式模型是一个很有前途的候选者,但迄今为止只研究了其在频率主义框架中的性能。在这项研究中,我们的目标是通过提出效应参数的先验分布,并研究模型对尺度参数的不同先验规范的稳健性,使用于罕见事件荟萃分析的β二项式模型符合贝叶斯推断。为了评估具有不同先验的贝叶斯β二项式模型的性能,我们用两个不同的数据生成模型进行了一项模拟研究,其中我们改变了合并效应的大小、异质性程度、基线概率和样本量。我们的结果表明,虽然在数据极为稀疏的荟萃分析中使用贝叶斯β二项式时必须谨慎,但在平均偏差、均方误差和覆盖率方面,对效应参数使用弱信息先验是有益的。对于量表参数,在使用贝叶斯β二项式模型对罕见事件进行的荟萃分析中,将半正态分布和指数分布确定为候选先验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Rare events meta-analysis using the Bayesian beta-binomial model

Rare events meta-analysis using the Bayesian beta-binomial model

In meta-analyses of rare events, it can be challenging to obtain a reliable estimate of the pooled effect, in particular when the meta-analysis is based on a small number of studies. Recent simulation studies have shown that the beta-binomial model is a promising candidate in this situation, but have thus far only investigated its performance in a frequentist framework. In this study, we aim to make the beta-binomial model for meta-analysis of rare events amenable to Bayesian inference by proposing prior distributions for the effect parameter and investigating the models' robustness to different specifications of priors for the scale parameter. To evaluate the performance of Bayesian beta-binomial models with different priors, we conducted a simulation study with two different data generating models in which we varied the size of the pooled effect, the degree of heterogeneity, the baseline probability, and the sample size. Our results show that while some caution must be exercised when using the Bayesian beta-binomial in meta-analyses with extremely sparse data, the use of a weakly informative prior for the effect parameter is beneficial in terms of mean bias, mean squared error, and coverage. For the scale parameter, half-normal and exponential distributions are identified as candidate priors in meta-analysis of rare events using the Bayesian beta-binomial model.

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来源期刊
Research Synthesis Methods
Research Synthesis Methods MATHEMATICAL & COMPUTATIONAL BIOLOGYMULTID-MULTIDISCIPLINARY SCIENCES
CiteScore
16.90
自引率
3.10%
发文量
75
期刊介绍: Research Synthesis Methods is a reputable, peer-reviewed journal that focuses on the development and dissemination of methods for conducting systematic research synthesis. Our aim is to advance the knowledge and application of research synthesis methods across various disciplines. Our journal provides a platform for the exchange of ideas and knowledge related to designing, conducting, analyzing, interpreting, reporting, and applying research synthesis. While research synthesis is commonly practiced in the health and social sciences, our journal also welcomes contributions from other fields to enrich the methodologies employed in research synthesis across scientific disciplines. By bridging different disciplines, we aim to foster collaboration and cross-fertilization of ideas, ultimately enhancing the quality and effectiveness of research synthesis methods. Whether you are a researcher, practitioner, or stakeholder involved in research synthesis, our journal strives to offer valuable insights and practical guidance for your work.
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