Statistics Seminars: Issues in ROC surface analysis with an application to externally validated cognition in Parkinson disease screening
19 July 2011 14:15 in TBC
The diagnostic accuracy of the Montreal Cognitive Assessment (MoCA) has been established recently for screening externally validated cognition in Parkinson's disease (PD) (Dalrymple-Alford et al, 2010). Patients were classified as having either normal cognition (PD-N), mild cognitive impairment (PD-MCI), or dementia (PD-D). ROC curve methodology has been used to assess discrimination between two adjacent classes and the Youden index has been employed for cut-off point selection. ROC surface methodology has also been used for the assessment of the simultaneous discrimination of the three classes. Recently, Nakas et al (2010) proposed a generalization of the Youden index for the assessment of accuracy and cut-off point selection in simultaneous discrimination of three classes. In this work, we examine properties of the generalized Youden index and compare two- vs. three-class classification accuracy approaches when screening for cognition status in PD.
Dalrymple-Alford, J.C., Macaskill, M.R., Nakas, C.T., et al. (2010). The MoCA: Well suited screen for cognitive impairment in Parkinson Disease. Neurology, 75, 1717-1725.
Nakas, C.T., Alonzo, T.A. And Yiannoutsos, C.T. (2010). Accuracy and cut-off point selection in three-class classification problems using a generalization of the Youden index. Statistics in Medicine, 29, 2946-2955.
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