A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations
A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations
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DOI：
10.1002/sim.942
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发表时间：
2001-10-15
影响因子：
2
通讯作者：
Gatsonis, CA
中科院分区：
文献类型：
--
作者：
Rutter, CM;Gatsonis, CA
用于研究综合的元分析模型的一个重要特性是它们能够解释研究内和研究间的变异性。目前可用的元分析方法的诊断测试准确性的研究工作主要是在一个固定效应的框架。在本文中，我们描述了一个分层回归模型的荟萃分析的研究报告的估计测试的敏感性和特异性。该模型允许更多的研究间和研究内的变异性比固定效应的方法，通过允许测试的严格性和测试的准确性，以不同的研究。也可以检查研究特定协变量的影响。使用马尔可夫链蒙特卡罗模拟和公开可用的软件（BUGS）计算估计值。这种估计方法允许灵活选择汇总统计量。我们使用最近发表的荟萃分析比较了三种用于检测宫颈癌淋巴结转移的检测方法，证明了这种建模方法的优势。版权所有（C）2001约翰威利父子有限公司
An important quality of meta-analytic models for research synthesis is their ability to account for both within- and between-study variability. Currently available meta-analytic approaches for studies of diagnostic test accuracy work primarily within a fixed-effects framework. In this paper we describe a hierarchical regression model for meta-analysis of studies reporting estimates of test sensitivity and specificity. The model allows more between- and within-study variability than fixed-effect approaches, by allowing both test stringency and test accuracy to vary across studies. It is also possible to examine the effects of study specific covariates. Estimates are computed using Markov Chain Monte Carlo simulation with publicly available software (BUGS). This estimation method allows flexibility in the choice of summary statistics. We demonstrate the advantages of this modelling approach using a recently published meta-analysis comparing three tests used to detect nodal metastasis of cervical cancer. Copyright (C) 2001 John Wiley & Sons, Ltd.