Rutter C M, Gatsonis C A
Group Health Cooperative, Center for Health Studies, 1730 Minor Avenue, Suite 1600, Seattle, WA 98101, USA.
Stat Med. 2001 Oct 15;20(19):2865-84. doi: 10.1002/sim.942.
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.
用于研究综合分析的元分析模型的一个重要特性是它们能够兼顾研究内部和研究之间的变异性。目前用于诊断试验准确性研究的元分析方法主要在固定效应框架内进行。在本文中，我们描述了一种用于对报告试验敏感性和特异性估计值的研究进行元分析的分层回归模型。该模型比固定效应方法允许更多的研究间和研究内变异性，因为它允许试验严格性和试验准确性在不同研究中有所变化。还可以检验研究特定协变量的影响。估计值使用公开可用软件（BUGS）通过马尔可夫链蒙特卡罗模拟计算得出。这种估计方法在汇总统计量的选择上具有灵活性。我们通过最近发表的一项比较用于检测宫颈癌淋巴结转移的三种试验的元分析来展示这种建模方法的优势。
