Prediction-based classification for longitudinal biomarkers

DOI10.1214/10-AOAS326zbMath1202.62152arXiv1011.3351OpenAlexW3098548095WikidataQ34518252 ScholiaQ34518252MaRDI QIDQ614167

Xiao-Hong Li, Andrea S. Foulkes, Colette Smith, Margaret A. Johnson, Luis J. Montaner, Livio Azzoni, Karam Mounzer

Publication date: 27 December 2010

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1011.3351

zbMATH Keywords

predictionclassificationHIV/AIDSCD4generalized linear mixed effects modeling (GLMM)receiver operator characteristic (ROC) curve

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50)

Related Items (1)

Optimal Allocation of Gold Standard Testing Under Constrained Availability: Application to Assessment of HIV Treatment Failure

Cites Work

• Random-Effects Models for Longitudinal Data
• Characterizing the Relationship Between HIV-1 Genotype and Phenotype: Prediction-Based Classification
• Semiparametric Regression for the Area Under the Receiver Operating Characteristic Curve
• Three Approaches to Regression Analysis of Receiver Operating Characteristic Curves for Continuous Test Results
• Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker
• An Interpretation for the ROC Curve and Inference Using GLM Procedures
• Random Effects Modeling Approaches for Estimating ROC Curves from Repeated Ordinal Tests without a Gold Standard
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