Bayesian nonparametric inference for the three-class Youden index and its associated optimal cut-points

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Pages (from-to)689-700
Number of pages13
JournalStatistical Methods in Medical Research
Volume27
Issue number3
Early online date15 Dec 2017
DOIs
Publication statusPublished - 1 Mar 2018

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