Quantifying how diagnostic test accuracy depends on threshold in a meta-analysis
Quantifying how diagnostic test accuracy depends on threshold in a meta-analysis
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DOI：
10.1002/sim.8301
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发表时间：
2019-10-30
影响因子：
2
通讯作者：
Ades, A. E.
中科院分区：
文献类型：
--
作者：
Jones, Hayley E.;Gatsonsis, Constantine A.;Ades, A. E.
对疾病的检测通常会产生连续的测量，例如血液样本中某些生物标志物的浓度。在临床实践中，选择阈值C，这样，例如，大于C的结果被宣布为阳性，小于C的结果被宣布为阴性。测试准确性的测量，如敏感性和特异性，关键取决于C，该阈值的最佳值通常是临床实践的关键问题。测试准确性的荟萃分析的标准方法(i)没有提供每个阈值的准确性的汇总估计，排除了最佳阈值的选择，而且（ii）没有利用所有可用的数据。我们描述了一个多项荟萃分析模型，该模型可以从每项研究中获取任意数量的敏感性和特异性对，并明确量化准确性对c的依赖程度。我们的模型假设，在患病和无病人群中，测试结果的一些预先指定或Box-Cox转换具有逻辑分布。Box-Cox变换参数可以从数据中估计出来，允许灵活的底层分布范围。我们根据两个逻辑分布的均值和尺度参数进行参数化。除了对所有阈值的综合敏感性和特异性的可信区间外，我们还产生了预测区间，允许所有参数的研究间异质性。我们使用两个案例研究荟萃分析来证明该模型，检查急性心力衰竭和先兆子痫测试的准确性。我们展示了如何将模型扩展到使用研究水平协变量来探索异质性的原因。
Tests for disease often produce a continuous measure, such as the concentration of some biomarker in a blood sample. In clinical practice, a threshold C is selected such that results, say, greater than C are declared positive and those less than C negative. Measures of test accuracy such as sensitivity and specificity depend crucially on C, and the optimal value of this threshold is usually a key question for clinical practice. Standard methods for meta-analysis of test accuracy (i) do not provide summary estimates of accuracy at each threshold, precluding selection of the optimal threshold, and furthermore, (ii) do not make use of all available data. We describe a multinomial meta-analysis model that can take any number of pairs of sensitivity and specificity from each study and explicitly quantifies how accuracy depends on C. Our model assumes that some prespecified or Box-Cox transformation of test results in the diseased and disease-free populations has a logistic distribution. The Box-Cox transformation parameter can be estimated from the data, allowing for a flexible range of underlying distributions. We parameterise in terms of the means and scale parameters of the two logistic distributions. In addition to credible intervals for the pooled sensitivity and specificity across all thresholds, we produce prediction intervals, allowing for between-study heterogeneity in all parameters. We demonstrate the model using two case study meta-analyses, examining the accuracy of tests for acute heart failure and preeclampsia. We show how the model can be extended to explore reasons for heterogeneity using study-level covariates.