Replicability Across Multiple Studies

IF 3.9 1区 数学 Q1 STATISTICS & PROBABILITY Statistical Science Pub Date : 2023-11-01 DOI:10.1214/23-sts892
Marina Bogomolov, Ruth Heller
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引用次数: 5

Abstract

Meta-analysis is routinely performed in many scientific disciplines. This analysis is attractive since discoveries are possible even when all the individual studies are underpowered. However, the meta-analytic discoveries may be entirely driven by signal in a single study, and thus nonreplicable. Although the great majority of meta-analyses carried out to date do not infer on the replicability of their findings, it is possible to do so. We provide a selective overview of analyses that can be carried out towards establishing replicability of the scientific findings. We describe methods for the setting where a single outcome is examined in multiple studies (as is common in systematic reviews of medical interventions), as well as for the setting where multiple studies each examine multiple features (as in genomics applications). We also discuss some of the current shortcomings and future directions.
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跨多个研究的可重复性
元分析在许多科学学科中是常规的。这种分析是有吸引力的,因为即使在所有单独的研究都不足的情况下,发现也是可能的。然而,元分析的发现可能完全是由单一研究中的信号驱动的,因此不可复制。尽管迄今为止进行的绝大多数荟萃分析都没有推断出他们的发现的可重复性,但这样做是可能的。我们提供了一个选择性的分析概述,可以朝着建立科学发现的可重复性进行。我们描述了在多个研究中检查单个结果的设置(如在医疗干预的系统评价中常见)以及多个研究每个检查多个特征的设置(如在基因组学应用中)的方法。我们还讨论了目前的一些不足和未来的发展方向。
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
Statistical Science
Statistical Science 数学-统计学与概率论
CiteScore
6.50
自引率
1.80%
发文量
40
审稿时长
>12 weeks
期刊介绍: The central purpose of Statistical Science is to convey the richness, breadth and unity of the field by presenting the full range of contemporary statistical thought at a moderate technical level, accessible to the wide community of practitioners, researchers and students of statistics and probability.
期刊最新文献
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