Work

Multi-category diagnostic accuracy based on logistic regression

Year: 2017

Type: article

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two categories, many traditional accuracy measures such as sensitivity, specificity and area under the ROC curve are no longer applicable.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.

Source: Statistical Theory and Related Fields

DOI: 10.1080/24754269.2017.1319105

Authors: Jialiang Li, Jason P. Fine, Michael J. Pencina

Institutions: National University of Singapore, Singapore Eye Research Institute, Duke-NUS Medical School, University of North Carolina at Chapel Hill, Duke University

Language: English

Topic: Statistical Methods in Epidemiology

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: National Natural Science Foundation of China, Medical Research Council, National Medical Research Council