Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies.
Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies.
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DOI：
10.1186/s12874-016-0196-1
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发表时间：
2016-08-12
影响因子：
4
通讯作者：
Rücker G
中科院分区：
文献类型：
--
作者：
Steinhauser S;Schumacher M;Rücker G
在诊断测试准确性的荟萃分析中，通常每个研究只使用一对敏感性和特异性。然而，对于基于生物标记物或问卷的测试，通常不止一个阈值以及相应的真阳性、真阴性、假阳性和假阴性值是已知的。我们利用这些附加信息提出了一种新的元分析方法。它基于在非患病和患病个体中估计潜在生物标志物或问卷的分布函数的想法。假设正态分布或逻辑分布，我们对转换后的数据应用线性混合效应模型来估计两组中的分布参数。该模型考虑了跨研究的异质性以及敏感性和特异性的依赖性。此外，还进行了仿真研究。我们得到了一个总的接受者工作特征（SROC）曲线，以及在每个特定阈值下的综合灵敏度和特异性。此外，通过最大化约登指数，确定跨研究的最佳阈值是可能的。我们通过两项荟萃分析证明了我们的方法：心力衰竭中的B型利钠肽和作为败血症标志物的降钙素原。我们的方法使用了所有可用的信息和结果，不仅对生物标志物的性能进行了估计，而且对预期最佳性能的阈值进行了估计。本文的在线版本（doi:10.1186/s12874-016-0196-1）包含补充材料，可供授权用户使用。
In meta-analyses of diagnostic test accuracy, routinely only one pair of sensitivity and specificity per study is used. However, for tests based on a biomarker or a questionnaire often more than one threshold and the corresponding values of true positives, true negatives, false positives and false negatives are known. We present a new meta-analysis approach using this additional information. It is based on the idea of estimating the distribution functions of the underlying biomarker or questionnaire within the non-diseased and diseased individuals. Assuming a normal or logistic distribution, we estimate the distribution parameters in both groups applying a linear mixed effects model to the transformed data. The model accounts for across-study heterogeneity and dependence of sensitivity and specificity. In addition, a simulation study is presented. We obtain a summary receiver operating characteristic (SROC) curve as well as the pooled sensitivity and specificity at every specific threshold. Furthermore, the determination of an optimal threshold across studies is possible through maximization of the Youden index. We demonstrate our approach using two meta-analyses of B type natriuretic peptide in heart failure and procalcitonin as a marker for sepsis. Our approach uses all the available information and results in an estimation not only of the performance of the biomarker but also of the threshold at which the optimal performance can be expected. The online version of this article (doi:10.1186/s12874-016-0196-1) contains supplementary material, which is available to authorized users.