|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|
|Keywords||Bayes 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 Journal||Stat Methods Med Res|