Statistics Seminars: ROC curve predictive inference for best linear combination of two biomarkers subject to limits of detection
10 December 2015 15:00 in Elvet Hill House, room EH113
Measuring the accuracy of diagnostic tests is crucial in many application
areas including medicine, machine learning and credit scoring. The receiver operating characteristic (ROC) curve is a useful tool to assess the ability of a diagnostic test to discriminate among two classes or groups. In practice biomarkers measurements are undetectable either below or above some limits, so called limit of detection. Taking this into account when considering the accuracy of a diagnostic test is of interest. In addition, multiple diagnostic tests or biomarkers are often combined to improve diagnostic accuracy. In this paper, nonparametric predictive inference (NPI) for a diagnostic test subject to limits of detection is presented. We then considered NPI for the best linear combination of two biomarkers subject to limits of detection. NPI is a frequentist statistical method that is explicitly aimed at using few modelling assumptions, enabled through the use of lower and upper probabilities to quantify uncertainty.
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