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
Volume27
Issue3
Pagination689-700
Date Published03/2018
ISSN1477-0334
KeywordsBayes Theorem, Biomarkers, Biostatistics, Cognitive Dysfunction, Computer Simulation, Disease Progression, Humans, Parkinson Disease, ROC Curve, Statistics, Nonparametric
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.

DOI10.1177/0962280217742538
Alternate JournalStat Methods Med Res
PubMed ID29241400