Nonparametric predictive inference for diagnostic test thresholds

Coolen‐Maturi, Tahani; Coolen, Frank P. A.; Alabdulhadi, Manal H.

doi:10.1080/03610926.2018.1549249

Cited by 4 publications

(14 citation statements)

References 63 publications

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“…Such methods can also be used in the case of many ordinal categories, if we cannot obtain perfect observations. Furthermore, NPI has been presented for direct selection of the optimal thresholds of a diagnostic test based on multiple future observations, which shows some promising results compared with the use of Youden's index for three‐group classification problems (Coolen‐Maturi et al ., ). Extending that for ordinal data and for more than three groups is an interesting topic.…”

Section: Discussionmentioning

confidence: 97%

Non-parametric Predictive Inference for the Validation of Credit Rating Systems

Coolen‐Maturi

Coolen

2018

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary Credit rating or credit scoring systems are important tools for estimating the obligor's creditworthiness and for providing an indication of the obligor's future status. The discriminatory power of a credit rating or credit scoring system refers to its ex ante ability to distinguish between two or more classes of borrowers. One of the most popular tools for the validation of the power of credit rating or credit scoring models to distinguish between two (or more) classes of borrowers is the receiver operating characteristic (ROC) curve (hypersurface) and its widely used overall summary, the area (hypervolume) under the curve (hypersurface). As the end goal of building such models is to predict and quantify uncertainty about future loans, prediction methods are especially valuable in this context. For this, non‐parametric predictive inference is a promising candidate for such inference as it 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 aim of the paper is to introduce non‐parametric predictive inference for ROC analysis within a banking context, for which novel results on ROC hypersurfaces for more than three groups are presented. Examples are provided to illustrate the method.

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Section: Discussionmentioning

confidence: 97%

Non-parametric Predictive Inference for the Validation of Credit Rating Systems

Coolen‐Maturi

Coolen

2018

Journal of the Royal Statistical Society Series A: Statistics in Society

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“…This provides a natural link to diagnostic tests which provide real-valued outcomes, with an optimal threshold determined on the basis of the data and some optimality criterion. We have recently presented NPI methods for determination of an optimal diagnostic threshold for such a scenario [1,16], and this motivated us to develop the method presented in this paper. We also considered comparison of two diagnostic tests which are restricted to the real-valued case, and with criterion to maximize the NPI lower or upper probability of correctly classifying at least two specified proportions of the future individuals from the healthy and diseased group [2].…”

Section: Comparison Of Tests Using Npi For Bernoulli Quantitiesmentioning

confidence: 99%

“…The first two examples use made-up data in order to illustrate the approach and discuss its important features. Example 3 uses data from the literature and is linked to an application of our recently presented NPI method to determine the optimal diagnostic threshold for real-valued data [1,16]. Tables 1 and 2 present the NPI lower and upper probabilities for the events…”

Section: Examplesmentioning

confidence: 99%

“…To illustrate our method for comparison of two diagnostic tests, we first decided on the optimal diagnostic threshold for each test. To stay within the NPI framework, we applied the recently presented method [1,16] where we choose the threshold which maximizes the NPI lower probability that at least half of the m x future individuals from group X will be correctly diagnosed, and also at least half of the m y future individuals from group Y. Throughout this example, we set m x = m y = m .…”

Section: Examplementioning

confidence: 99%

“…Coolen-Maturi [15] considered NPI for scenarios where two or more diagnostic tests are combined in order to improve the overall accuracy. Recently, we have presented NPI methods for determining an optimal test threshold for diagnostic tests with real-valued outcomes, explicitly considering given numbers of future individuals from the healthy and disease groups [1,16]. As an alternative to the approach in this paper, we have also presented comparison of two diagnostic tests with real-valued outcomes for diagnostic tests with two or three groups of individuals, where the comparisons consider the NPI lower and upper probabilities of the events that at least a specified proportion of future individuals, for each of the groups, will be correctly classified [2].…”

Section: Introductionmentioning

confidence: 99%

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Nonparametric Predictive Comparison of Two Diagnostic Tests Based on Total Numbers of Correctly Diagnosed Individuals

Alabdulhadi

Coolen

Coolen‐Maturi

2019

J Stat Theory Pract

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In clinical applications, it is important to compare and study the ability of diagnostic tests to discriminate between individuals with and without the disease. In this paper, comparison of two diagnostic tests is presented and discussed using nonparametric predictive inference (NPI). We compare the two tests by considering the total numbers of correct diagnoses for specific numbers of future healthy individuals and future patients. This NPI approach for comparison of diagnostic tests is also generalized by the use of weighted sums for the healthy and patients groups, reflecting possibly different importance of correct diagnoses. Examples are provided to illustrate the new method.

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