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在元回归模型中使用截距描述估计值:移动常数法。

Depicting estimates using the intercept in meta-regression models: The moving constant technique.

作者信息

Johnson Blair T, Huedo-Medina Tania B

机构信息

Department of Psychology, 406 Babbidge Road Unit 1020, University of Connecticut, Storrs, CT 06269-1020, USA; Center for Health, Intervention, and Prevention 2006 Hillside Road Unit 1248, University of Connecticut, Storrs, CT, 06269-1248, USA.

出版信息

Res Synth Methods. 2011 Sep;2(3):204-20. doi: 10.1002/jrsm.49.

Abstract

In any scientific discipline, the ability to portray research patterns graphically often aids greatly in interpreting a phenomenon. In part to depict phenomena, the statistics and capabilities of meta-analytic models have grown increasingly sophisticated. Accordingly, this article details how to move the constant in weighted meta-analysis regression models (viz. "meta-regression") to illuminate the patterns in such models across a range of complexities. Although it is commonly ignored in practice, the constant (or intercept) in such models can be indispensible when it is not relegated to its usual static role. The moving constant technique makes possible estimates and confidence intervals at moderator levels of interest as well as continuous confidence bands around the meta-regression line itself. Such estimates, in turn, can be highly informative to interpret the nature of the phenomenon being studied in the meta-analysis, especially when a comparison with an absolute or a practical criterion is the goal. Knowing the point at which effect size estimates reach statistical significance or other practical criteria of effect size magnitude can be quite important. Examples ranging from simple to complex models illustrate these principles. Limitations and extensions of the strategy are discussed. Copyright © 2011 John Wiley & Sons, Ltd.

摘要

在任何科学学科中,以图形方式描绘研究模式的能力通常对解释一种现象有很大帮助。部分为了描述现象,元分析模型的统计方法和功能变得越来越复杂。因此,本文详细介绍了如何在加权元分析回归模型(即“元回归”)中移动常数,以阐明此类模型在一系列复杂情况下的模式。尽管在实践中它通常被忽视,但当此类模型中的常数(或截距)不被限定在其通常的静态角色时,它可能是不可或缺的。移动常数技术使得在感兴趣的调节变量水平上进行估计和构建置信区间成为可能,同时也能围绕元回归线本身构建连续的置信带。反过来,这样的估计对于解释元分析中所研究现象的本质可能具有很高的参考价值,尤其是当目标是与绝对标准或实际标准进行比较时。了解效应量估计达到统计显著性或其他效应量大小的实际标准的点可能非常重要。从简单模型到复杂模型的例子说明了这些原理。文中还讨论了该策略的局限性和扩展。版权所有© 2011约翰·威利父子有限公司。

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