Nonparametric Predictive Inference for diagnostic accuracy

Communications in Statistics - Theory and Methods

2016

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine, machine learning and credit scoring. The receiver operating characteristic (ROC) surface is a useful tool to assess the ability of a diagnostic test to discriminate among three ordered classes or groups. In this paper, nonparametric predictive inference (NPI) for three-group ROC analysis for ordinal outcomes 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. This paper also includes results on the volumes under the ROC surfaces and consideration of the choice of decision thresholds for the diagnosis. Two examples are provided to illustrate our method.

Section: Introductionmentioning

confidence: 99%

Three-group ROC predictive analysis for ordinal outcomes

Communications in Statistics - Theory and Methods

2016

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“…Due to the importance of prediction of the accuracy of diagnostic tests for a future patient, NPI provides an attractive alternative approach to the established methods in this field. NPI has recently been introduced for assessing the accuracy of a classifier's ability to discriminate between two groups for binary data (Coolen-Maturi et al, 2012a), ordinal data (Elkhafifi and Coolen, 2012) and real-valued data (Coolen-Maturi et al, 2012b). This paper introduces NPI for three-group ROC analysis for real-valued data.…”

Section: Introductionmentioning

confidence: 99%

Three-group ROC analysis: A nonparametric predictive approach

Computational Statistics & Data Analysis

Elkhafifi

Coolen

2014

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. (2014) Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractMeasuring the accuracy of diagnostic tests is crucial in many application areas, in particular medicine and health care. The receiver operating characteristic (ROC) surface is a useful tool to assess the ability of a diagnostic test to discriminate among three ordered classes or groups. In this paper, nonparametric predictive inference (NPI) for three-group ROC analysis is presented. NPI is a frequentist statistical method that is explicitly aimed at using few modelling assumptions in addition to data, enabled through the use of lower and upper probabilities to quantify uncertainty. It focuses exclusively on a future observation, which may be particularly relevant if one considers decisions about a diagnostic test to be applied to a future patient. This paper presents the NPI approach to three-group ROC analysis, including results on the volumes under the ROC surfaces and choice of decision thresholds for the diagnosis.

“…Recently, [24] generalized the results in [23] by introducing NPI for three-group ROC analysis, with real-valued observations, to assess the ability of a diagnostic test to discriminate among three ordered classes or groups. Coolen-Maturi [25] generalized the results in [22] by proposing NPI for three-group ROC analysis with ordinal outcomes.…”

Section: Nonparametric Predictive Inferencementioning

confidence: 99%

“…Coolen-Maturi [25] generalized the results in [22] by proposing NPI for three-group ROC analysis with ordinal outcomes. Below we give a brief overview of NPI for two-group ROC analysis following [23].…”

Section: Nonparametric Predictive Inferencementioning

confidence: 99%

See 1 more Smart Citation

Predictive inference for best linear combination of biomarkers subject to limits of detection

2017

Statistics in Medicine

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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. Copyright © 2017 John Wiley & Sons, Ltd.