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Multi-category diagnostic accuracy based on logistic regression

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  • Jialiang Li
  • Jason P. Fine
  • Michael J. Pencina

Abstract

We provide a detailed review for the statistical analysis of diagnostic accuracy in a multi-category classification task. For qualitative response variables with more than two categories, many traditional accuracy measures such as sensitivity, specificity and area under the ROC curve are no longer applicable. In recent literature, new diagnostic accuracy measures are introduced in medical research studies. In this paper, important statistical concepts for multi-category classification accuracy are reviewed and their utilities are demonstrated with real medical examples. We offer problem-based R code to illustrate how to perform these statistical computations step by step. We expect such analysis tools will become more familiar to practitioners and receive broader applications in biostatistics. Our program can be adapted to many classifiers among which logistic regression may be the most popular approach. We thus base our discussion and illustration completely on the logistic regression in this paper.

Suggested Citation

  • Jialiang Li & Jason P. Fine & Michael J. Pencina, 2017. "Multi-category diagnostic accuracy based on logistic regression," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 1(2), pages 143-158, July.
    • Handle: RePEc:taf:tstfxx:v:1:y:2017:i:2:p:143-158
    DOI: 10.1080/24754269.2017.1319105
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