Construction of confidence regions in the ROC space after the estimation of the optimal Youden index‐based cut‐off point
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Publication:4979252
DOI10.1111/BIOM.12107zbMath1419.62307OpenAlexW2159193885WikidataQ44490145 ScholiaQ44490145MaRDI QIDQ4979252
Benjamin Reiser, Christos T. Nakas, Leonidas E. Bantis
Publication date: 17 June 2014
Published in: Biometrics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/biom.12107
Parametric tolerance and confidence regions (62F25) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric tolerance and confidence regions (62G15)
Related Items (7)
Joint inference about sensitivity and specificity at the optimal cut-off point associated with Youden index ⋮ Improved nonparametric estimation of the optimal diagnostic cut-off point associated with the Youden index under different sampling schemes ⋮ Construction of joint confidence spaces for the optimal true class fraction triplet in the ROC space using alternative biomarker cutoffs ⋮ Likelihood-type confidence regions for optimal sensitivity and specificity of a diagnostic test ⋮ Exact inference for disease prevalence based on a test with unknown specificity and sensitivity ⋮ Statistical inference for the difference between two maximized Youden indices obtained from correlated biomarkers ⋮ Construction of confidence intervals for the maximum of the Youden index and the corresponding cutoff point of a continuous biomarker
Cites Work
- Estimation of the diagnostic threshold accounting for decision costs and sampling uncertainty
- A study of logspline density estimation
- Confidence Intervals for the Youden Index and Corresponding Optimal Cut-Point
- Asymptotic and qualitative performance of non-parametric density estimators: a comparative study
- A family of nonparametric statistics for comparing diagnostic markers with paired or unpaired data
- The Large-Sample Behavior of Transformations to Normality
- Two transformation models for estimating an ROC curve derived from continuous data
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