Modelling of ROC curves in meta-analysis of diagnostic test accuracy studies and network meta-analysis

该项目涉及医学证据合成的两个活跃研究领域，即诊断测试准确性研究的荟萃分析和网络荟萃分析。最终的目标是将这两个领域结合起来。在诊断准确性研究的荟萃分析的标准方法中，每项研究都被认为贡献了一对敏感性和特异性。在项目的第一阶段，我们研究了两种更普遍的方法，这两种方法解释了潜在生物标记物的多个阈值，“模拟生物标记物分布”和“平均ROC曲线”。第一种方法是基于估计潜在生物标记物在未患病和患病个体中的分布函数的思想。基于分布假设，我们对适当变换的数据应用线性混合效应模型来估计两组中的分布参数。该模型既考虑了研究内对敏感性和特异性的依赖性，又考虑了研究间的异质性。我们得到了一个概要的接收者工作特征(SROC)曲线，以及在每个特定阈值下的综合灵敏度和特异度。在Martinez-Camblor(2014)首次提出的第二种方法中，总结ROC曲线是通过对主要研究的ROC曲线进行“垂直”方向的平均(即，根据特异性来平均敏感度)来确定的。我们通过(1)交换敏感度和特异度的角色，即以敏感度为条件来平均特异度(水平平均)，以及(2)以真阳性率和假阳性率之和为条件(对角线平均)来平均真阳性率和假阳性率(即Youden指数)，从而扩展了该方法。在四个出版物中，我们研究了(1)NMA和电网络理论之间的关系，(2)通过适当膨胀标准误差来调整多臂研究的替代方法，(3)网络自动可视化的方法，以及(4)基于网络荟萃分析的频率治疗排名。在第二个项目阶段，我们希望完善和扩展所有这些方法，特别关注知识转换。一方面，先进方法的范围越来越广，另一方面，它们对非统计用户的可及性有限。我们希望弥合这一差距，并继续遵循这一目标，编写一个新的R包来实现‘模型化生物标记物分布’和‘平均ROC曲线’方法，并扩展我们现有的R包NETMETA用于网络荟萃分析，例如通过荟萃回归。最终目标是将DTA研究的Meta分析与NMA结合起来，形成DTA研究的Meta分析网络。

This project refers to two active research areas of evidence synthesis in medicine, meta-analysis of diagnostic test accuracy studies and network meta-analysis. The final objective is to combine both areas.In standard approaches of meta-analysis of diagnostic accuracy studies, each study is assumed to contribute one pair of sensitivity and specificity. In the first period of the project, we investigated two more general approaches, which account for multiple thresholds of the underlying biomarker, `Modelling biomarker distributions' and `Averaging ROC curves'.The first approach is based on the idea of estimating the distribution functions of an underlying biomarker within the non-diseased and diseased individuals. Based on a distributional assumption, we estimate the distribution parameters in the two groups applying a linear mixed effects model to the appropriately transformed data. The model accounts for both the within-study dependence of sensitivity and specificity and between-study heterogeneity. 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.In the second approach, originally introduced by Martinez-Camblor (2014), the summary ROC curve is determined as a weighted average of the ROC curves of the primary studies by averaging in `vertical' direction (i.e., averaging sensitivities, conditional on specificity). We extended this method by (1) exchanging the roles of sensitivity and specificity, i.e., averaging specificities conditional on sensitivity (horizontal averaging), and (2) averaging the differences of true positive rates and false positive rates (i.e., the Youden indices) conditional on their sum (diagonal averaging).Another area of research was network meta-analysis (NMA). In four publications, we investigated (1) the relation between NMA and electrical network theory, (2) an alternative method of adjusting for multi-arm studies by appropriately inflating the standard errors, (3) methods of automated visualisation of networks, and (4) frequentist treatment ranking, based on a network-meta-analysis.In the second project period we want to refine and extend all these approaches, with a special focus on knowledge translation. There is a more and more increasing spectrum of advanced methods on the one hand and their restricted accessibility for non-statistical users on the other hand. We want to bridge this gap and continue to follow this aim by writing a new R package for implementation of the `Modelling biomarker distributions' and `Averaging ROC curves' approaches and extending our existing R package netmeta for network meta-analysis, for example by meta-regression. The final objective is to combine meta-analysis of DTA studies and NMA to network meta-analysis of DTA studies.