A Flexible Method for Diagnostic Accuracy with Biomarker Measurement Error

Diagnostic biomarkers are often measured with errors due to imperfect lab conditions or analytic variability of the assay. The ability of a diagnostic biomarker to discriminate between cases and controls is often measured by the area under the receiver operating characteristic curve (AUC), sensitivi...

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Bibliographic Details
Published in:Mathematics (Basel) 2023-02, Vol.11 (3), p.549
Main Authors: Wang, Ching-Yun, Feng, Ziding
Format: Article
Language:English
Subjects:
Accuracy and precision
Analysis
Approximation
Assaying
Bias
Biological markers
Biomarkers
Biopsy
correction for attenuation
Decision making
Diagnosis
Diagnostic systems
Error analysis
Error analysis (Mathematics)
Estimation
Industrial development
Measurement
measurement error
Methods
Normal distribution
Pancreatic cancer
Performance evaluation
Random variables
Reproducibility
Sensitivity
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Summary:Diagnostic biomarkers are often measured with errors due to imperfect lab conditions or analytic variability of the assay. The ability of a diagnostic biomarker to discriminate between cases and controls is often measured by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, among others. Ignoring measurement error can cause biased estimation of a diagnostic accuracy measure, which results in misleading interpretation of the efficacy of a diagnostic biomarker. Existing assays available are either research grade or clinical grade. Research assays are cost effective, often multiplex, but they may be associated with moderate measurement errors leading to poorer diagnostic performance. In comparison, clinical assays may provide better diagnostic ability, but with higher cost since they are usually developed by industry. Correction for attenuation methods are often valid when biomarkers are from a normal distribution, but may be biased with skewed biomarkers. In this paper, we develop a flexible method based on skew-normal biomarker distributions to correct for bias in estimating diagnostic performance measures including AUC, sensitivity, and specificity. Finite sample performance of the proposed method is examined via extensive simulation studies. The methods are applied to a pancreatic cancer biomarker study.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11030549