Nonparametric Predictive Inference for diagnostic accuracy |
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| Authors: | Tahani Coolen-Maturi Frank P.A. Coolen |
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| Affiliation: | a Kent Business School, University of Kent, Canterbury, Kent CT2 7PE UK
b Department of Mathematical Sciences, Durham University, Durham DH1 3LE, UK |
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| Abstract: | Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine and health care. Good methods for determining diagnostic accuracy provide useful guidance on selection of patient treatment, and the ability to compare different diagnostic tests has a direct impact on quality of care. In this paper Nonparametric Predictive Inference (NPI) methods for accuracy of diagnostic tests with continuous test results are presented and discussed. For such tests, Receiver Operating Characteristic (ROC) curves have become popular tools for describing the performance of diagnostic tests. We present the NPI approach to ROC curves, and some important summaries of these curves. As NPI does not aim at inference for an entire population but instead explicitly considers a future observation, this provides an attractive alternative to standard methods. We show how NPI can be used to compare two continuous diagnostic tests. |
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| Keywords: | Area under the ROC curve (AUC) Diagnostic accuracy Lower and upper probability Nonparametric Predictive Inference (NPI) Partial area under the ROC curve (pAUC) Receiver Operating Characteristic (ROC) |
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