Department of Biostatistics, Erasmus MC - Erasmus University Medical Center, Rotterdam, the Netherlands.
BMC Med Res Methodol. 2009 Nov 10;9:73. doi: 10.1186/1471-2288-9-73.
Bivariate random effects meta-analysis of diagnostic tests is becoming a well established approach when studies present one two-by-two table or one pair of sensitivity and specificity. When studies present multiple thresholds for test positivity, usually meta-analysts reduce the data to a two-by-two table or take one threshold value at a time and apply the well developed meta-analytic approaches. However, this approach does not fully exploit the data.
In this paper we generalize the bivariate random effects approach to the situation where test results are presented with k thresholds for test positivity, resulting in a 2 by (k+1) table per study. The model can be fitted with standard likelihood procedures in statistical packages such as SAS (Proc NLMIXED). We follow a multivariate random effects approach; i.e., we assume that each study estimates a study specific ROC curve that can be viewed as randomly sampled from the population of all ROC curves of such studies. In contrast to the bivariate case, where nothing can be said about the shape of study specific ROC curves without additional untestable assumptions, the multivariate model can be used to describe study specific ROC curves. The models are easily extended with study level covariates.
The method is illustrated using published meta-analysis data. The SAS NLMIXED syntax is given in the appendix.
We conclude that the multivariate random effects meta-analysis approach is an appropriate and convenient framework to meta-analyse studies with multiple threshold without losing any information by dichotomizing the test results.
当研究呈现一个两列两行的表格或一对灵敏度和特异性时，双变量随机效应荟萃分析已成为诊断测试的一种成熟方法。当研究呈现多个测试阳性的阈值时，通常荟萃分析人员会将数据简化为两列两行的表格，或者一次采用一个阈值，并应用成熟的荟萃分析方法。然而，这种方法并没有充分利用数据。
在本文中，我们将双变量随机效应方法推广到研究呈现 k 个测试阳性阈值的情况，每个研究产生一个 2 乘 (k+1) 表格。该模型可以使用统计软件包（如 SAS（Proc NLMIXED））中的标准似然程序进行拟合。我们采用多元随机效应方法；也就是说，我们假设每个研究估计一个特定于研究的 ROC 曲线，可以将其视为从该研究的所有 ROC 曲线的总体中随机抽样的曲线。与双变量情况不同，在没有额外不可检验假设的情况下，无法对特定于研究的 ROC 曲线的形状进行任何说明，多元模型可用于描述特定于研究的 ROC 曲线。该模型可以轻松地与研究水平的协变量扩展。
使用已发表的荟萃分析数据说明了该方法。SAS NLMIXED 语法在附录中给出。
我们得出结论，多元随机效应荟萃分析方法是一种合适且方便的框架，可以对具有多个阈值的研究进行荟萃分析，而不会通过将测试结果二分法而丢失任何信息。
