Bayesian nonparametric inference for the three-class Youden index and its associated optimal cutoff points. (March 2018)

• Record Type: Journal Article
• Authors: Carvalho, Vanda Inácio de; Branscum, Adam J
• Other Names: Nakas Christos T guest-editor.; Reiser Benjamin guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 27:Number 3(2018)
• Page Start: 689
• Page End: 700
• Publication Date: 2018-03
• Subjects: Diagnostic test -- Dirichlet process mixtures -- optimal cutoff -- true class fraction -- Youden index; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280217742538
• Languages: English
• ISSNs: 0962-2802

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.