ROC Analysis for Classification and Prediction in Practice
1st Edition
Published on 15. May 2023
234 pages
978-1-4822-3371-1 (ISBN)
Description
This book presents a unified and up-to-date introduction to ROC methodologies, covering both diagnosis (classification) and prediction. The emphasis is on the conceptual underpinning of ROC analysis and the practical implementation in diverse scientific fields. A plethora of examples accompany the methodologic discussion using standard statistical software such as R and STATA. The book arrives after two decades of intensive growth in both the methods and the applications of ROC analysis and presents a new synthesis. The authors provide a contemporary, integrated exposition of ROC methodology for both classification and prediction and include material on multiple-class ROC. This book avoids lengthy technical exposition and provides code and datasets in each chapter. ROC Analysis for Classification and Prediction in Practice is intended for researchers and graduate students, but will also be useful for those that use ROC analysis in diverse disciplines such as diagnostic medicine, bioinformatics, medical physics, and perception psychology.
Reviews / Votes
"This book fills a critical gap. I could not find another reference on the ROC curve as comprehensive as the one by Nakas, Bantis, and Gatsonis. This book should be recommended as an excellent reference textbook for anyone needing an in-depth understanding of the ROC curve or for a specialized graduate course." - Mauricio Tec, Journal of the American Statistical Association"One of the book's strengths is the inclusion of R and Stata examples throughout, with code blocks conveniently integrated-and helpfully color-coded based on the software. Each chapter concludes with a limited selection of exercises, suggesting that the authors conceptualize this text as a possible basis for an advanced special topics course for PhD-level students. An instructor for a special topics course would likely assign several of the original research articles referenced in this text as required reading."
-Andrew J. Spieker and Nathaniel P. Dowd, Biometrics, 2025
"Altogether, the book is a solid technical handbook for all aspects of ROC analysis..."
-Sebastian Dietz, Journal of the Royal Statistical Society Series A: Statistics in Society, November, 2025.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
Philadelphia, PA
United States
Publishing group
Taylor & Francis Inc
Target group
Professional and scholarly
Illustrations
16 Tables, black and white; 15 Line drawings, color; 20 Line drawings, black and white; 3 Halftones, color; 18 Illustrations, color; 20 Illustrations, black and white
ISBN-13
978-1-4822-3371-1 (9781482233711)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Christos Nakas
ROC Analysis for Classification and Prediction in Practice
Book
05/2023
1st Edition
€132.40
Available (delivery time upon request)
Persons
Christos T Nakas is Full Professor in Biometry at the University of Thessaly, Volos, Greece, and Primary Investigator/Consultant for Biostatistics and Data Science at the Department of Clinical Chemistry (UKC), Inselspital, University Hospital of the University of Bern, Bern, Switzerland. His research revolves around ROC analysis, Statistical testing/modeling, methods of Agreement, and their applications in Medicine, and Life Sciences disciplines in general.
Leonidas E Bantis is Assistant Professor in Biostatistics at the Department of Biostatistics and Data Science, University of Kansas Medical Center, and a member of the University of Kansas Cancer Center, Kansas City, KS, USA. His research focus lies on the development of methods related to marker discovery, evaluation, modeling, and comparisons. He is primarily interested in the mathematical aspects and different metrics that are involved in the receiver operating characteristic (ROC) space.
Constantine A Gatsonis is Henry Ledyard Goddard University Professor of Biostatistics, at Brown University School of Public Health, Providence, RI, U.S.A. He is the founding Chair of the Department of Biostatistics and founding Director of the Center for Statistical Sciences at Brown. Dr. Gatsonis is a leading authority on the evaluation of diagnostic and screening tests, and has made major contributions to the development of methods for medical technology assessment and health services and outcomes research. He is a world leader in methods for applying and synthesizing evidence on diagnostic tests in medicine and is currently developing methods for Comparative Effectiveness Research in diagnosis and prediction, and radiomics.
Leonidas E Bantis is Assistant Professor in Biostatistics at the Department of Biostatistics and Data Science, University of Kansas Medical Center, and a member of the University of Kansas Cancer Center, Kansas City, KS, USA. His research focus lies on the development of methods related to marker discovery, evaluation, modeling, and comparisons. He is primarily interested in the mathematical aspects and different metrics that are involved in the receiver operating characteristic (ROC) space.
Constantine A Gatsonis is Henry Ledyard Goddard University Professor of Biostatistics, at Brown University School of Public Health, Providence, RI, U.S.A. He is the founding Chair of the Department of Biostatistics and founding Director of the Center for Statistical Sciences at Brown. Dr. Gatsonis is a leading authority on the evaluation of diagnostic and screening tests, and has made major contributions to the development of methods for medical technology assessment and health services and outcomes research. He is a world leader in methods for applying and synthesizing evidence on diagnostic tests in medicine and is currently developing methods for Comparative Effectiveness Research in diagnosis and prediction, and radiomics.
Content
1. Introduction 2. Measures of Diagnostic and Predictive Performance 3. Statistical inference for the ROC curve 4. Comparing ROC curves 5. The ROC surface and k-class classification for k > 2 6. ROC regression 7. Missing data and errors-in-variables in ROC analysis
