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ROC analysis in biomarker combination with covariate adjustment
1Biostatistics and Bioinformatics Branch, Division of Epidemiology, Statistics & Prevention Research, NIH/NICHD, 6100 Executive Blvd., Bethesda, MD 20892, USA. danping.liu@nih.gov
Academic Radiology
|June 11, 2013
Summary
Two new methods, optimal area under the covariate-adjusted ROC curve (AAUC) and area under covariate-standardized ROC curve (SAUC), improve biomarker combination by accounting for subject covariates. SAUC is preferred for its generalizability across populations.
Area of Science:
- Biostatistics
- Biomarker Discovery
- Medical Informatics
Background:
- Receiver operating characteristic (ROC) analysis is crucial for optimizing biomarker combinations.
- Subject-level covariates can influence biomarker accuracy and magnitude, necessitating covariate adjustment in combination rules.
Purpose of the Study:
- To propose novel biomarker combination methods incorporating covariate information.
- To address limitations in existing methods by introducing covariate adjustment.
Main Methods:
- Developed two methods: maximizing area under the covariate-adjusted ROC curve (AAUC) and area under covariate-standardized ROC curve (SAUC).
- Conducted simulation studies comparing optimal AAUC and SAUC with a standard optimal AUC method (ignoring covariates).
- Applied methods to an Alzheimer's disease research example.
Main Results:
- Optimal AAUC demonstrated good performance within the study population.
- Optimal SAUC proved flexible, allowing for generalization to different reference populations.
Conclusions:
- Proposed optimal AAUC and SAUC methods effectively handle covariate adjustment in biomarker combination.
- Optimal SAUC is recommended for practical application due to its adaptability for evaluating different populations of interest.
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