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Meta-analysis of the validity of binary diagnoses based on dichotomized cirteria

基于二分标准的二元诊断有效性的荟萃分析

基本信息

批准号：
58856953
负责人：
Professor Dr. Heinz Holling
金额：
--
依托单位：
Arbeitseinheit Statistik und Methoden
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2008
资助国家：
德国
起止时间：
2007-12-31 至 2016-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/58856953?language=en
关键词：
Meta analysis validity binary diagnoses

项目摘要

In the first phase of the project two new meta-analytic models for diagnostic studies were developed that explicitly incorporated the cut-off value problem. The first model, a proportional hazards model (PHM), is based upon the Lehmann-family for SROC curves, whereas the second model uses a bivariate normal distribution of logarithmically transformed sensitivities and specificities. In the second phase of this project, these models were extended to allow for covariate information, to explain heterogeneity in the random effects model. Furthermore, a parametric link function was included in the PHM. Hence this approach is embedded in a larger class of models with rather flexible SROC curves. To make these methodologies readily available for a larger user community the R-package mada has been developed.The models developed so far allow the analysis of observed heterogeneity (by means of covariates) as well as unobserved heterogeneity (by means of random effect models). In this third phase, for which one year is available, unobserved heterogeneity will be further quantified. As the diversity of collected meta-analyses shows, almost every available meta-analysis of diagnostic studies shows some form of unobserved heterogeneity. However, the strength of heterogeneity varies considerably across the various meta-analyses. Conventionally, the magnitude of heterogeneity is measured by means of the heterogeneity measure I2, suggested by Higgins und Thompson (2002). This quantity, however, is not a classical effect measure as it is not independent of the sample sizes of the individual studies. Here, a modified heterogeneity measure is suggested for diagnostic meta-analyses, which does not suffer under the above limitation. The development of this new heterogeneity measure has emerged through the findings of the second phase of the project. Furthermore, the properties and qualities of this new heterogeneity measure will be investigated analytically and by means of simulation studies.

在项目的第一阶段，开发了两个新的诊断研究的元分析模型，明确纳入了截止值问题。第一个模型，比例风险模型（PHM），是基于莱曼家族的SROC曲线，而第二个模型使用双变量正态分布的几何变换的敏感性和特异性。在该项目的第二阶段，这些模型被扩展到允许协变量信息，以解释随机效应模型中的异质性。此外，PHM中还包含了一个参数连接函数。因此，这种方法被嵌入到一个更大的一类模型，而灵活的SROC曲线。为了使这些方法易于为更大的用户社区使用，开发了R包mada。迄今开发的模型允许分析观察到的异质性（通过协变量）以及未观察到的异质性（通过随机效应模型）。在第三阶段（为期一年），将进一步量化未观察到的异质性。正如所收集的荟萃分析的多样性所显示的那样，几乎所有可用的诊断研究荟萃分析都显示出某种形式的未观察到的异质性。然而，异质性的强度在各种荟萃分析中差异很大。传统上，异质性的大小通过Higgins und Thompson（2002）建议的异质性度量I2来测量。然而，这一数量并不是一个经典的效应指标，因为它并不独立于个体研究的样本量。在这里，一个修改后的异质性措施，建议诊断荟萃分析，不受上述限制。通过项目第二阶段的调查结果，已经出现了这种新的异质性衡量标准的发展。此外，这个新的异质性措施的属性和质量将研究分析和模拟研究的手段。