ArticleStatistics in medicine2017
Estimation of smooth ROC curves for biomarkers with limits of detection.
Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Estimating transformations for evaluating diagnostic tests with covariate adjustment.Statistical methods in medical research · 2023Article
- Statistical assessment of the prognostic and the predictive value of biomarkers-A biomarker assessment framework with applications to traumatic brain injury biomarker studies.Research methods in medicine & health sciences · 2023Article
- A family of estimators to diagnostic accuracy when candidate tests are subject to detection limits-Application to diagnosing early stage Alzheimer disease.Statistical methods in medical research · 2022Article
- Machine learning-based genome-wide interrogation of somatic copy number aberrations in circulating tumor DNA for early detection of hepatocellular carcinoma.EBioMedicine · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Protein biomarkers found in plasma are commonly used for cancer screening and early detection. Measurements obtained by such markers are often based on different assays that may not support detection of accurate measurements due to a limit of detection. The ROC curve is the most popular statistical tool for the evaluation of a continuous biomarker. However, in situations where limits of detection exist, the empirical ROC curve fails to provide a valid estimate for the whole spectrum of the false positive rate (FPR). Hence, crucial information regarding the performance of the marker in high sensitivity and/or high specificity values is not revealed. In this paper, we address this problem and propose methods for constructing ROC curve estimates for all possible FPR values. We explore flexible parametric methods, transformations to normality, and robust kernel-based and spline-based approaches. We evaluate our methods though simulations and illustrate them in colorectal and pancreatic cancer data.
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Registered trials
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