A nonparametric "trim and fill" method of accounting for publication bias in meta-analysis
A nonparametric "trim and fill" method of accounting for publication bias in meta-analysis
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DOI：
10.2307/2669529
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发表时间：
2000-03-01
影响因子：
3.7
通讯作者：
Tweedie, R
中科院分区：
文献类型：
--
作者：
Duval, S;Tweedie, R
荟萃分析收集和综合来自个体研究的结果，以估计总体效应量。如果选择已发表的研究，比如通过文献综述，那么可能会出现固有的选择偏差，因为，例如，如果研究具有统计学意义，或者在其结果的影响方面被认为更“有趣”，则可能更容易发表。我们开发了一种简单的基于秩的数据增强技术，正式使用漏斗图，估计和调整缺失研究的数量和结果。缺失研究的数量提出了几个非参数估计，并通过分析和模拟开发其属性。我们将该方法应用于模拟和流行病学数据集，并表明它是有效的，并与文献中的其他标准相一致。
Meta-analysis collects and synthesizes results from individual studies to estimate an overall effect size. If published studies are chosen, say through a literature review, then an inherent selection bias may arise, because, for example, studies may tend to be published more readily if they are statistically significant, or deemed to be more "interesting" in terms of the impact of their outcomes. We develop a simple rank-based data augmentation technique, formalizing the use of funnel plots, to estimate and adjust for the numbers and outcomes of missing studies. Several nonparametric estimators are proposed for the number of missing studies, and their properties are developed analytically and through simulations. We apply the method to simulated and epidemiological datasets and show that it is both effective and consistent with other criteria in the literature.