对具有选择性报告和依赖效应大小的荟萃分析进行稳健的点和方差估计

IF 6.3 2区 环境科学与生态学 Q1 ECOLOGY Methods in Ecology and Evolution Pub Date : 2024-07-09 DOI:10.1111/2041-210X.14377
Yefeng Yang, Malgorzata Lagisz, Coralie Williams, Daniel W. A. Noble, Jinming Pan, Shinichi Nakagawa
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引用次数: 0

摘要

荟萃分析是以证据为基础的知识的定量综合,不仅影响研究趋势,还影响生物学的政策和实践。然而,选择性报告和统计依赖性这两个统计问题会严重扭曲荟萃分析的参数估计和推断。在此,我们重新分析了 448 项元分析,展示了一种新的两步程序,以应对生物元分析中经常同时出现的两个常见挑战:发表偏倚和非独立性。首先,我们在广义最小二乘法估计器下采用偏倚稳健加权方案,以获得对选择性报告更稳健的平均效应大小。然后,我们使用集群稳健方差估计来考虑统计依赖性,从而减少标准误差估计中的偏差,确保有效的统计推断。在存在选择性报告的情况下,我们方法的第一步在估计平均效应大小方面的表现与现有的发表偏倚调整方法相当。这种等效性在两种发表偏倚选择过程中都适用。第二步实现了与多层次元分析模型一致的标准误差估算,这是一种基准方法,可充分控制I类错误率,以处理多个统计依赖效应大小。对 448 项元分析的重新分析表明,忽略这两个问题往往会使效应大小平均高估 110%,标准误差平均低估 120%。为了便于实施,我们开发了一个网站,其中包括分步教程。用提出的方法作为敏感性分析来补充当前的荟萃分析工作流程,可以促进定量证据综合向更稳健的方法过渡。
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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Robust point and variance estimation for meta-analyses with selective reporting and dependent effect sizes

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来源期刊
CiteScore
11.60
自引率
3.00%
发文量
236
审稿时长
4-8 weeks
期刊介绍: A British Ecological Society journal, Methods in Ecology and Evolution (MEE) promotes the development of new methods in ecology and evolution, and facilitates their dissemination and uptake by the research community. MEE brings together papers from previously disparate sub-disciplines to provide a single forum for tracking methodological developments in all areas. MEE publishes methodological papers in any area of ecology and evolution, including: -Phylogenetic analysis -Statistical methods -Conservation & management -Theoretical methods -Practical methods, including lab and field -This list is not exhaustive, and we welcome enquiries about possible submissions. Methods are defined in the widest terms and may be analytical, practical or conceptual. A primary aim of the journal is to maximise the uptake of techniques by the community. We recognise that a major stumbling block in the uptake and application of new methods is the accessibility of methods. For example, users may need computer code, example applications or demonstrations of methods.
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