Nonparametric Predictive Inference for Binary Diagnostic Tests

Abstract

Measuring the accuracy of diagnostic tests is crucial in many application areas, including medicine, health care, and data mining. 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) for accuracy of diagnostic tests with binary test results is presented and discussed, together with methods for comparison of two such tests. NPI does not aim at inference for an entire population but instead explicitly considers future observations, which is particularly suitable for inference to support decisions on medical diagnosis for one future patient, or for a predetermined number of future patients, so the NPI approach provides an attractive alternative to standard methods.

Nonparametric Predictive Inference for Reproducibility of Basic Nonparametric Tests

“…In addition to the scenarios used in this article, NPI has, for example, been presented for multinomial data and lifetime data with right-censored observations (Coolen 2011). Recently presented NPI methods for statistical inference and decision support considered, for example, quality control (Arts et al 2004;Arts and Coolen 2008), precedence testing (Coolen-Schrijner et al 2009), accuracy of diagnostic tests (Coolen-Maturi et al 2012;Elkhafifi and Coolen 2012), and acceptance decisions (Coolen and Elsaeiti 2009;Elsaeiti and Coolen 2012).…”

Three-group ROC predictive analysis for ordinal outcomes

“…In NPI, attention is restricted to one or more future observable random quantities, and Hill's assumption A (n) (Hill, 1968) is used to link these random quantities to data, in a way that is closely related to exchangeability (De Finetti, 1974). NPI has been introduced for assessing the accuracy of a classifier's ability to discriminate between two outcomes (or two groups) for binary data (Coolen-Maturi et al, 2012a) and for diagnostic tests with ordinal observations (Elkhafifi and Coolen, 2012) and with real-valued observations (Coolen-Maturi et al, 2012b). Recently, Coolen-Maturi et al (2014) generalized the results in (CoolenMaturi et al, 2012b) by introducing NPI for three-group ROC surface, with real-valued observations, to assess the ability of a diagnostic test to discriminate among three ordered classes or groups.…”

Three-group ROC analysis: A nonparametric predictive approach

“…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.…”