A tutorial on aggregating evidence from conceptual replication studies using the product Bayes factor.

IF 5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Research Synthesis Methods Pub Date : 2024-11-01 Epub Date: 2024-10-23 DOI:10.1002/jrsm.1765
Caspar J Van Lissa, Eli-Boaz Clapper, Rebecca Kuiper
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引用次数: 0

Abstract

The product Bayes factor (PBF) synthesizes evidence for an informative hypothesis across heterogeneous replication studies. It can be used when fixed- or random effects meta-analysis fall short. For example, when effect sizes are incomparable and cannot be pooled, or when studies diverge significantly in the populations, study designs, and measures used. PBF shines as a solution for small sample meta-analyses, where the number of between-study differences is often large relative to the number of studies, precluding the use of meta-regression to account for these differences. Users should be mindful of the fact that the PBF answers a qualitatively different research question than other evidence synthesis methods. For example, whereas fixed-effect meta-analysis estimates the size of a population effect, the PBF quantifies to what extent an informative hypothesis is supported in all included studies. This tutorial paper showcases the user-friendly PBF functionality within the bain R-package. This new implementation of an existing method was validated using a simulation study, available in an Online Supplement. Results showed that PBF had a high overall accuracy, due to greater sensitivity and lower specificity, compared to random-effects meta-analysis, individual participant data meta-analysis, and vote counting. Tutorials demonstrate applications of the method on meta-analytic and individual participant data. The example datasets, based on published research, are included in bain so readers can reproduce the examples and apply the code to their own data. The PBF is a promising method for synthesizing evidence for informative hypotheses across conceptual replications that are not suitable for conventional meta-analysis.

使用乘积贝叶斯因子汇总概念复制研究证据的教程。
乘积贝叶斯因子(PBF)综合了异质性重复研究中某一信息假设的证据。当固定效应荟萃分析或随机效应荟萃分析无法满足要求时,可以使用该方法。例如,当效应大小不可比且无法汇集时,或者当研究在使用的人群、研究设计和测量方法上存在显著差异时。PBF 是小样本荟萃分析的理想解决方案,在这种情况下,相对于研究数量,研究间差异的数量往往很大,因此无法使用荟萃回归来解释这些差异。用户应注意,与其他证据综合方法相比,PBF 所回答的研究问题在质量上有所不同。例如,固定效应荟萃分析估计的是群体效应的大小,而PBF量化的是所有纳入的研究在多大程度上支持了一个信息假设。本教程论文展示了 bain R 软件包中用户友好的 PBF 功能。对现有方法的这一新实施通过模拟研究进行了验证,结果见在线增刊。结果表明,与随机效应荟萃分析、单个参与者数据荟萃分析和投票计数相比,PBF 具有更高的灵敏度和更低的特异性,因此总体准确率较高。教程演示了该方法在荟萃分析和个体参与者数据上的应用。bain 中包含了基于已发表研究的示例数据集,读者可以复制示例并将代码应用到自己的数据中。PBF 是一种很有前途的方法,它可以在不适合传统荟萃分析的概念复制中综合信息假设的证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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