ROC Analysis for Classification and Prediction in Practice  book cover
1st Edition

ROC Analysis for Classification and Prediction in Practice

  • Available for pre-order on April 5, 2023. Item will ship after April 26, 2023
ISBN 9781482233704
April 26, 2023 Forthcoming by Chapman & Hall
232 Pages 18 Color & 20 B/W Illustrations

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USD $120.00

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Book Description

This book will present a unified and up-to date introduction to ROC methodologies, covering both diagnosis (classification) and prediction. The book will emphasize the practical implementation of these methods using standard statistical software such as R and STATA. Existing books tend to be specialized and/or focus on the theoretical derivations, with limited discussion of the use of the concepts and methods across diverse scientific fields and modest emphasis on the implementation of the methods. This book comes after more than a decade of intensive growth in both the methods and the applications of ROC analysis. It is time for a new synthesis. This book will provide a contemporary, integrated exposition of ROC methodology for both classification and prediction, and will include material on multiple class ROC. It will avoid lengthy technical exposition and will provide code and datasets in each chapter. Receiver Operating Characteristic Analysis for Classification and Prediction is primarily for researchers, but will also be useful for those that use ROC analysis in disciplines such as diagnostic medicine, bioinformatics, medical physics, and perception psychology.

Key Features:

  • Description of basic ROC methodology
  • R and STATA code
  • Example Datasets
  • Not too technical
  • Many topics not included in other books

Table of Contents

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

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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.