A discrete time-to-event model for the meta-analysis of full ROC curves

IF 5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Ferdinand Valentin Stoye, Claudia Tschammler, Oliver Kuss, Annika Hoyer
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Abstract

The development of new statistical models for the meta-analysis of diagnostic test accuracy studies is still an ongoing field of research, especially with respect to summary receiver operating characteristic (ROC) curves. In the recently published updated version of the “Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy”, the authors point to the challenges of this kind of meta-analysis and propose two approaches. However, both of them come with some disadvantages, such as the nonstraightforward choice of priors in Bayesian models or the requirement of a two-step approach where parameters are estimated for the individual studies, followed by summarizing the results. As an alternative, we propose a novel model by applying methods from time-to-event analysis. To this task we use the discrete proportional hazard approach to treat the different diagnostic thresholds, that provide means to estimate sensitivity and specificity and are reported by the single studies, as categorical variables in a generalized linear mixed model, using both the logit- and the asymmetric cloglog-link. This leads to a model specification with threshold-specific discrete hazards, avoiding a linear dependency between thresholds, discrete hazard, and sensitivity/specificity and thus increasing model flexibility. We compare the resulting models to approaches from the literature in a simulation study. While the estimated area under the summary ROC curve is estimated comparably well in most approaches, the results depict substantial differences in the estimated sensitivities and specificities. We also show the practical applicability of the models to data from a meta-analysis for the screening of type 2 diabetes.

Abstract Image

用于全 ROC 曲线荟萃分析的离散时间到事件模型。
为诊断测试准确性研究的荟萃分析开发新的统计模型仍是一个持续的研究领域,尤其是在接收者操作特征曲线(ROC)汇总方面。在最近出版的《Cochrane 诊断测试准确性系统综述手册》更新版中,作者指出了此类荟萃分析所面临的挑战,并提出了两种方法。然而,这两种方法都有一些缺点,比如贝叶斯模型中先验值的选择并不简单,或者需要分两步进行,即先估计单个研究的参数,然后再总结结果。作为一种替代方法,我们提出了一种应用时间到事件分析方法的新型模型。为此,我们采用了离散比例危险法,将不同的诊断阈值作为分类变量,在广义线性混合模型中使用 logit 和非对称 cloglog 链接来处理,这些阈值提供了估算敏感性和特异性的方法,并由单项研究报告。这导致了一种具有阈值特异性离散危害的模型规范,避免了阈值、离散危害和灵敏度/特异性之间的线性依赖关系,从而提高了模型的灵活性。在模拟研究中,我们将得出的模型与文献中的方法进行了比较。虽然大多数方法都能很好地估算出 ROC 曲线下的估计面积,但结果表明在估计灵敏度和特异性方面存在很大差异。我们还展示了这些模型在 2 型糖尿病筛查荟萃分析数据中的实际应用性。
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来源期刊
Research Synthesis Methods
Research Synthesis Methods MATHEMATICAL & COMPUTATIONAL BIOLOGYMULTID-MULTIDISCIPLINARY SCIENCES
CiteScore
16.90
自引率
3.10%
发文量
75
期刊介绍: Research Synthesis Methods is a reputable, peer-reviewed journal that focuses on the development and dissemination of methods for conducting systematic research synthesis. Our aim is to advance the knowledge and application of research synthesis methods across various disciplines. Our journal provides a platform for the exchange of ideas and knowledge related to designing, conducting, analyzing, interpreting, reporting, and applying research synthesis. While research synthesis is commonly practiced in the health and social sciences, our journal also welcomes contributions from other fields to enrich the methodologies employed in research synthesis across scientific disciplines. By bridging different disciplines, we aim to foster collaboration and cross-fertilization of ideas, ultimately enhancing the quality and effectiveness of research synthesis methods. Whether you are a researcher, practitioner, or stakeholder involved in research synthesis, our journal strives to offer valuable insights and practical guidance for your work.
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