Title | Bayesian nonparametric inference for the three-class Youden index and its associated optimal cutoff points. |
Publication Type | Journal Article |
Year of Publication | 2018 |
Authors | de Carvalho, VInácio, Branscum, AJ |
Journal | Stat Methods Med Res |
Volume | 27 |
Issue | 3 |
Pagination | 689-700 |
Date Published | 03/2018 |
ISSN | 1477-0334 |
Keywords | Bayes 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. |
DOI | 10.1177/0962280217742538 |
Alternate Journal | Stat Methods Med Res |
PubMed ID | 29241400 |