Dataset for: Quantifying how diagnostic test accuracy depends on threshold in a meta-analysis

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 further (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 pre-specified 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 pre-eclampsia. We show how the model can be extended to explore reasons for heterogeneity using study-level covariates.

疾病检测通常会生成连续型检测结果，例如血液样本中某类生物标志物的浓度。临床实践中，研究者会选定阈值C：当检测结果高于C时判定为阳性，低于C时则判定为阴性。检测效能指标（如灵敏度、特异度）的取值高度依赖阈值C，因此该阈值的最优值通常是临床实践中的核心问题。现有检测效能Meta分析的标准方法存在两大局限：其一，无法给出各阈值下的效能汇总估计值，从而无法实现最优阈值的筛选；其二，未能充分利用所有可用的研究数据。本文提出一种多项分布Meta分析模型，该模型可纳入每项研究中任意数量的灵敏度-特异度配对数据，并明确量化检测效能随阈值C的变化规律。本模型假设，患病与未患病人群的检测结果经预设转换或Box-Cox变换（Box-Cox transformation）后服从逻辑斯蒂分布（logistic distribution）。可通过研究数据估计Box-Cox变换的参数，从而适配多种潜在分布形态，具备良好的灵活性。我们以两个逻辑斯蒂分布的均值与尺度参数作为模型的参数化形式。除了可给出所有阈值下合并灵敏度与特异度的可信区间外，本模型还可生成预测区间，且允许所有参数存在研究间异质性。本文通过两项Meta分析案例验证该模型的实用性，分别用于评估急性心力衰竭与子痫前期检测的临床效能。同时演示了如何通过纳入研究层面的协变量，拓展该模型以探究异质性产生的根源。