Coolen-Maturi, T. (2017) 'Predictive inference for best linear combination of biomarkers subject to limits of detection.', Statistics in medicine., 36 (18). pp. 2844-2874.
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 between two classes or groups. In practice, multiple diagnostic tests or biomarkers are combined to improve diagnostic accuracy. Often, biomarker measurements are undetectable either below or above the so-called limits of detection (LoD). In this paper, nonparametric predictive inference (NPI) for best linear combination of two or more biomarkers subject to limits of detection is presented. 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. The NPI lower and upper bounds for the ROC curve subject to limits of detection are derived, where the objective function to maximize is the area under the ROC curve. In addition, the paper discusses the effect of restriction on the linear combination's coefficients on the analysis. Examples are provided to illustrate the proposed method.
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1002/sim.7317|
|Publisher statement:||This is the accepted version of the following article: Coolen-Maturi,T. (2017). Predictive inference for best linear combination of biomarkers subject to limits of detection. Statistics in Medicine, which has been published in final form at https://doi.org/10.1002/sim.7317. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.|
|Date accepted:||04 April 2017|
|Date deposited:||07 April 2017|
|Date of first online publication:||28 May 2017|
|Date first made open access:||28 May 2018|
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