Abstract
The three-class Youden index serves both as a measure of medical test accuracy and a criterion to choose the optimal pair of cutoff values for classifying subjects into three ordinal disease categories (e.g., no disease, mild disease, advanced disease). We present a Bayesian nonparametric approach for estimating the three-class Youden index and its corresponding optimal cutoff values based on Dirichlet process mixtures, which are robust models that can handle intricate features of distributions for complex data. Results from a simulation study are presented and an application to data from the Trail Making Test to assess cognitive impairment in Parkinson’s disease patients is detailed.
Original language | English |
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Pages (from-to) | 689-700 |
Number of pages | 13 |
Journal | Statistical Methods in Medical Research |
Volume | 27 |
Issue number | 3 |
Early online date | 15 Dec 2017 |
DOIs | |
Publication status | Published - 1 Mar 2018 |
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Vanda Calhau Fernandes Inacio De Carvalho
- School of Mathematics - Lectureships in Statistics
Person: Academic: Research Active