Confidence Distributions and a Unifying Framework for Meta-Analysis
Confidence Distributions and a Unifying Framework for Meta-Analysis
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DOI：
10.1198/jasa.2011.tm09803
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发表时间：
2011-03-01
影响因子：
3.7
通讯作者：
Strawderman, William E.
中科院分区：
文献类型：
--
作者：
Xie, Minge;Singh, Kesar;Strawderman, William E.
本文开发了一个统一的框架，以及强大的荟萃分析方法，结合来自独立来源的研究。该组合中使用的设备是置信分布（CD），其使用分布函数而不是点（点估计量）或区间（置信区间）来估计感兴趣的参数。CD函数包含大量用于推断的信息，并且它是用于组合来自不同来源的研究的有用设备。所提出的整合框架不仅统一了大多数现有的荟萃分析方法，但也导致了新的方法的发展。我们在这篇文章中说明，这种组合框架可以包括经典的方法相结合的p值和现代模型为基础的荟萃分析方法。我们还开发，统一的框架下，两个新的强大的荟萃分析方法，支持渐近理论。在一种方法中，每个研究的规模都是无限的。而在另一种方法中，研究的数量会无限大。我们的理论发展表明，这两种强大的荟萃分析方法都有很高的崩溃点，对正常模型非常有效。新方法适用于心脏病发作和胃溃疡治疗中预防性使用利多卡因的出版物的研究水平数据。当数据受到污染时，稳健方法表现良好，并且具有现实的样本量和研究数量。
This article develops a unifying framework, as well as robust meta-analysis approaches, for combining studies from independent sources. The device used in this combination is a confidence distribution (CD), which uses a distribution function, instead of a point (point estimator) or an interval (confidence interval), to estimate a parameter of interest. A CD function contains a wealth of information for inferences, and it is a useful device for combining studies from different sources. The proposed combining framework not only unifies most existing meta-analysis approaches, but also leads to development of new approaches. We illustrate in this article that this combining framework can include both the classical methods of combining p-values and modern model-based meta-analysis approaches. We also develop, under the unifying framework, two new robust meta-analysis approaches, with supporting asymptotic theory. In one approach each study size goes to infinity. and in the other approach the number of studies goes to infinity. Our theoretical development suggests that both these robust meta-analysis approaches have high breakdown points and are highly efficient for normal models. The new methodologies are applied to study-level data from publications on prophylactic use of lidocaine in heart attacks and a treatment of stomach ulcers. The robust methods performed well when data are contaminated and have realistic sample sizes and number of studies.