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Graphical Augmentations to the Funnel Plot to Assess the Impact of a New Study on an Existing Meta-Analysis

机译:图解到漏斗策划的增强,以评估新研究对现有Meta分析的影响

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摘要

Funnel plots are currently advocated to investigate the presence of publication bias (and other possible sources of bias) in meta-analysis. A previously described augmentation to the funnel plot—to aid its interpretation in assessing publication biases—is the addition of statistical contours indicating regions where studies would have to be for a given level of significance, as implemented in the Stata package confunnel by Palmer et al. (2008, Stata Journal 8: 242–254).ududIn this article, we describe the implementation of a new range of overlay augmentations to the funnel plot, many described in detail recently by Langan et al. (2012, Journal of Clinical Epidemiology 65: 511–519). The purpose of these overlays is to display the potential impact a new study would have on an existing meta-analysis, providing an indication of the robustness of the meta-analysis to the addition of new evidence. Thus these overlays extend the use of the funnel plot beyond assessments of publication biases. Two main graphical displays are described: 1) statistical significance contours, which define regions of the funnel plot where a new study would have to be located to change the statistical significance of the meta-analysis; and 2) heterogeneity contours, which show how a new study would affect the extent of heterogeneity in a given meta-analysis.ududWe present the extfunnel command, which implements the methods of Langan et al. (2012, Journal of Clinical Epidemiology 65: 511–519), and, furthermore, we extend the graphical displays to illustrate the impact a new study has on lower and upper confidence interval values and the confidence interval width of the pooled meta-analytic result. We also describe overlays for the impact of a future study on user-defined limits of clinical equivalence. We implement inversevariance weighted methods by using both explicit formulas for contour lines and a simulation approach optimized in Mata.
机译:目前主张漏斗情节旨在调查在Meta分析中的出版物偏差(和其他可能的偏差源)的存在。先前描述了对漏斗图的增强 - 以帮助其在评估出版物偏差方面的解释 - 添加统计轮廓,指示研究区域必须用于给定水平的意义,如帕尔默等人在STATA包汇总所实施的那样。 (2008年,Stata Journal 8:242-254)。 ud udin本文,我们描述了对漏斗图的新系列覆盖增强的实现,许多人最近由Langan等人详细描述。 (2012年,临床流行病学杂志65:511-519)。这些叠加的目的是显示新研究对现有的元分析的潜在影响,提供了荟萃分析的稳健性的指示,以增加新证据。因此,这些叠加将漏斗曲线的使用扩展到超出出版偏差的评估之外。描述了两个主要图形显示:1)统计显着性轮廓,其定义漏斗曲线区域,其中必须定位新的研究以改变元分析的统计学意义; 2)异质性轮廓,其展示了新的研究如何影响给定的元分析中的异质性程度。 UD Udwe介绍了extfunnel命令,它实现Langan等人的方法。 (2012年,临床流行病学杂志65:511-519),而且,我们延长了图形显示,以说明新研究对较低和上置信区间的影响以及汇集的元分析结果的置信区间宽度。我们还描述了未来研究对临床当量的用户定义限制的影响的叠加。我们通过使用两种明确的公式来实现Inversewariance加权方法,以进行轮廓线和Mata优化的仿真方法。

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