TitleBayesian nonparametric inference for the three-class Youden index and its associated optimal cutoff points.
Publication TypeJournal Article
Year of Publication2018
Authorsde Carvalho, VInácio, Branscum, AJ
JournalStat Methods Med Res
Date Published03/2018
KeywordsBayes Theorem, Biomarkers, Biostatistics, Cognitive Dysfunction, Computer Simulation, Disease Progression, Humans, Parkinson Disease, ROC Curve, Statistics, Nonparametric

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.

Alternate JournalStat Methods Med Res
PubMed ID29241400