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Journal of Statistical Theory and Practice

, Volume 6, Issue 4, pp 665–680 | Cite as

Nonparametric Predictive Inference for Binary Diagnostic Tests

  • Tahani Coolen-Maturi
  • Pauline Coolen-Schrijner
  • Frank P. A. Coolen
Article

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.

Keywords

Binary data Diagnostic test accuracy Effect size Lower and upper probability Nonparametric predictive inference Pairwise comparison 

AMS Subject Classification

60A99 62G99 62P10 

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Copyright information

© Grace Scientific Publishing 2012

Authors and Affiliations

  • Tahani Coolen-Maturi
    • 1
  • Pauline Coolen-Schrijner
    • 2
  • Frank P. A. Coolen
    • 2
  1. 1.Durham Business SchoolDurham UniversityDurhamUK
  2. 2.Department of Mathematical SciencesDurham UniversityDurhamUK

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