Analysis of clustered data in receiver operating characteristic studies.
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.
Beam
C A
CA
Northwestern University Medical School, Chicago, IL 60611-4402, USA.
D001499:Bayes Theorem; D016000:Cluster Analysis; D019937:Diagnostic Techniques and Procedures / Q000592:standards*; D012372:ROC Curve; D012044:Regression Analysis; D015203:Reproducibility of Results; D018709:Statistics, Nonparametric