Beam C A
Northwestern University Medical School, Chicago, IL 60611-4402, USA.
Stat Methods Med Res. 1998 Dec;7(4):324-36. doi: 10.1177/096228029800700402.
Clustered data is not simply correlated data, but has its own unique aspects. In this paper, various methods for correlated receiver operating characteristic (ROC) curve data that have been extended specifically to clustered data are reviewed. For those methods that have not yet been extended, suggestions for their application to clustered ROC studies are provided. Various methods with respect to their ability to meet either of two objectives of the analysis of clustered ROC data are compared to consider a variety of ROC indices and their accessibility to researchers. The available statistical methods for clustered data vary in the range of indices that can be considered and in their accessibility to researchers. Parametric models permit all indices to be considered but, owing to computational complexity, are the least accessible of available methods. Nonparametric methods are much more accessible, but only permit estimation and inference about ROC curve area. The jackknife method is the most accessible and permits any index to be considered. Future development of methods for clustered ROC studies should consider the continuation ratio model, which will permit the application of widely available software for the analysis of mixed generalized linear models. Another area of development should be in the adoption of bootstrapping methods to clustered ROC data.
聚类数据并非简单的相关数据，而是有其自身独特的方面。本文回顾了专门针对聚类数据扩展的相关接收器操作特性（ROC）曲线数据的各种方法。对于尚未扩展的那些方法，提供了将其应用于聚类ROC研究的建议。比较了各种方法在满足聚类ROC数据分析的两个目标之一的能力方面的情况，以考虑各种ROC指标及其对研究人员的可及性。用于聚类数据的现有统计方法在可考虑的指标范围及其对研究人员的可及性方面各不相同。参数模型允许考虑所有指标，但由于计算复杂性，是现有方法中最难使用的。非参数方法更易于使用，但仅允许对ROC曲线面积进行估计和推断。刀切法是最易于使用的，并且允许考虑任何指标。聚类ROC研究方法的未来发展应考虑连续比例模型，这将允许应用广泛可用的软件来分析混合广义线性模型。另一个发展领域应该是将自助法应用于聚类ROC数据。
