Multi-category diagnostic accuracy based on logistic regression
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 a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2017-07-01
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| Series: | Statistical Theory and Related Fields |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/24754269.2017.1319105 |
| _version_ | 1797677105371152384 |
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| author | Jialiang Li Jason P. Fine Michael J. Pencina |
| author_facet | Jialiang Li Jason P. Fine Michael J. Pencina |
| author_sort | Jialiang Li |
| collection | DOAJ |
| description | 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two categories, many traditional accuracy measures such as sensitivity, specificity and area under the ROC curve are no longer applicable.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. |
| first_indexed | 2024-03-11T22:40:11Z |
| format | Article |
| id | doaj.art-c437c6ca6f6547529eb529136ba6802f |
| institution | Directory Open Access Journal |
| issn | 2475-4269 2475-4277 |
| language | English |
| last_indexed | 2024-03-11T22:40:11Z |
| publishDate | 2017-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Statistical Theory and Related Fields |
| spelling | doaj.art-c437c6ca6f6547529eb529136ba6802f2023-09-22T09:19:44ZengTaylor & Francis GroupStatistical Theory and Related Fields2475-42692475-42772017-07-011214315810.1080/24754269.2017.13191051319105Multi-category diagnostic accuracy based on logistic regressionJialiang Li0Jason P. Fine1Michael J. Pencina2Singapore Eye Research Institute, National University of SingaporeUniversity of North CarolinaDuke UniversityWe 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.http://dx.doi.org/10.1080/24754269.2017.1319105hypervolume under the roc manifoldmulti-category classificationcorrect classification probabilitynet reclassification improvementintegrated discrimination improvementmarker evaluationr software |
| spellingShingle | Jialiang Li Jason P. Fine Michael J. Pencina Multi-category diagnostic accuracy based on logistic regression Statistical Theory and Related Fields hypervolume under the roc manifold multi-category classification correct classification probability net reclassification improvement integrated discrimination improvement marker evaluation r software |
| title | Multi-category diagnostic accuracy based on logistic regression |
| title_full | Multi-category diagnostic accuracy based on logistic regression |
| title_fullStr | Multi-category diagnostic accuracy based on logistic regression |
| title_full_unstemmed | Multi-category diagnostic accuracy based on logistic regression |
| title_short | Multi-category diagnostic accuracy based on logistic regression |
| title_sort | multi category diagnostic accuracy based on logistic regression |
| topic | hypervolume under the roc manifold multi-category classification correct classification probability net reclassification improvement integrated discrimination improvement marker evaluation r software |
| url | http://dx.doi.org/10.1080/24754269.2017.1319105 |
| work_keys_str_mv | AT jialiangli multicategorydiagnosticaccuracybasedonlogisticregression AT jasonpfine multicategorydiagnosticaccuracybasedonlogisticregression AT michaeljpencina multicategorydiagnosticaccuracybasedonlogisticregression |