Statistical Methods in Diagnostic Medicine - Gene A Pennello

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Auteur(s) : Gene A Pennello - Jiarui Sun - Xiao-Hua Zhou

Editeur : Wiley John + Sons

Langue : Anglais

Parution : 01/07/2026

Format : Moyen, de 350g à 1kg

Nombre de pages : 576.0

ISBN : 1394220219

Résumé :

The definitive resource for ensuring diagnostic tests meet the highest standards of statistical rigor and clinical effectiveness

Statistical Methods in Diagnostic Medicine, 3rd Edition by Xiao-Hua Zhou, Jiarui Sun, Gene A. Pennello, Nancy A. Obuchowski and Donna K. McClish delivers the most comprehensive treatment of statistical methodologies for diagnostic test evaluation available today. The authors of the 2nd Edition - Peking University PKU Distinguished Chair Professor Zhou, Cleveland Clinic Professor Obuchowski, and Virginia Commonwealth University Professor Donna McClish - team with U.S. Food and Drug Administration senior mathematical statistician Pennello and doctoral researcher Sun to address a critical challenge facing medical professionals: ensuring that diagnostic tests used in clinical practice are accurate, methodologically sound, free from bias, and effective.

This edition provides practitioners and researchers with the statistical foundation necessary to design, analyze, and validate diagnostic studies that can withstand regulatory scrutiny and clinical demands. The book has been thoroughly revised to incorporate the latest advances in diagnostic test methodology, featuring significant expansions in biomarker evaluation and benefit-risk assessment. The authors have restructured content to improve cohesion through integrated case studies that span multiple chapters, while updating each section with contemporary methods and streamlining discussions of older techniques to focus on the most relevant approaches for today's diagnostic challenges.

Readers will also find:

• Three entirely new chapters covering statistical methods for risk prediction, quantitative imaging biomarkers, and efficacy and effectiveness of biomarkers and other tests.
• Enhanced coverage of sample size calculations, accuracy estimation methods, and comparative analysis techniques for competing diagnostic tests
• Advanced analytical approaches including methods for comparing correlated ROC curves in multi-reader studies and techniques for correcting verification bias
• Comprehensive treatment of regression analysis applications in diagnostic accuracy research with updated methodological guidance
• Integrated case studies that demonstrate real-world application of statistical methods across different diagnostic scenarios and study designs

Perfect for biostatisticians, applied statisticians, clinical researchers, and regulatory professionals working in diagnostic medicine, Statistical Methods in Diagnostic Medicine will also benefit graduate students and researchers interested in gaining the statistical expertise needed to design robust diagnostic studies.

...

Biographie:

Xiao-Hua Zhou, fellow of the American Association for the Advancement of Science, fellow of the American Statistical Association, fellow of Institute of Mathematical Statistics, is PKU Distinguished Chair Professor and Chair of the Department of Biostatistics at Peking University, Beijing, China. His research focuses on statistical methods for diagnostic medicine, causal inference, and clinical trials, with extensive experience in regulatory statistics and biomedical research methodology. He has published more than 290 referred papers in those areas.

Jiarui Sun is Senior Biostatistician in Shanghai Shengdi Pharmaceutical Co., Ltd. and received his Ph.D from the School of Mathematical Science at Peking University, Beijing, China. His research interests include statistical methods for diagnostic accuracy studies, biomarker evaluation, and computational approaches to medical statistics and diagnostic test validation.

Gene A. Pennello, fellow of the American Statistical Association, is a statistical reviewer and research Mathematical Statistician at the U.S. Food and Drug Administration (FDA) in Silver Spring, Maryland. He specializes in statistical methods for medical device evaluation, diagnostic test assessment, and regulatory review processes for medical technologies.

Nancy A. Obuchowski, fellow of the American Statistical Association, Professor of Medicine at the Cleveland Clinic Lerner College of Medicine at Case Western Reserve University, has extensive experience in the design, analysis, and development of new statistical methodology for the evaluation of diagnostic and screening tests and quantitative imaging biomarkers.

Donna K. McClish, PhD, is Associate Professor and Graduate Program Director in Biostatistics at Virginia Commonwealth University. She has written more than 100 journal articles on statistical methods in epidemiology, diagnostic medicine, and health services research....

Sommaire:

Preface xiv

Acknowledgments xvi

Part I Basic Concepts and Methods 1

1 Introduction 3

1.1 Diagnostic Test Accuracy Studies 3

1.2 Case Studies 5

1.2.1 Case Study 1: Parathyroid Disease 5

1.2.2 Case Study 2: Colon Cancer Detection 6

1.2.3 Case Study 3: Carotid Artery Stenosis 7

1.3 Software 8

1.4 Topics Not Covered in This Book 8

2 Measures of Diagnostic Accuracy 9

2.1 Sensitivity and Specificity 9

2.1.1 Basic Measures of Test Accuracy: Case Study 2 11

2.1.2 Diagnostic Tests with Continuous Results: The Artificial Heart Valve Example 12

2.1.3 Diagnostic Tests with Ordinal Results: Case Study 1 13

2.1.4 Effect of Prevalence and Spectrum of Disease 14

2.1.5 Analogy to ? and ? Statistical Errors 14

2.2 Combined Measures of Sensitivity and Specificity 15

2.2.1 Problems Comparing Two or More Tests: Case Study 1 15

2.2.2 Probability of a Correct Test Result 15

2.2.3 Odds Ratio and Youden's Index 16

2.3 ROC Curve 17

2.3.1 ROC Curves: Artificial Heart Valve and Case Study 1 17

2.3.2 ROC Curve Assumption 18

2.3.3 Smooth, Fitted ROC Curves 19

2.3.4 Advantages of ROC Curves 19

2.4 Area Under the ROC Curve 20

2.4.1 Interpretation of the Area Under the ROC Curve 20

2.4.2 Magnitudes of the Area Under the ROC Curve 21

2.4.3 Area Under the ROC Curve: Case Study 1 21

2.4.4 Misinterpretations of the Area Under the ROC Curve 23

2.5 Sensitivity at Fixed FPR 25

2.6 Partial Area Under the ROC Curve 25

2.7 Likelihood Ratios 26

2.7.1 Three Examples to Illustrate Likelihood Ratios 27

2.7.2 Limitations of Likelihood Ratios 28

2.7.3 Proper and Improper ROC Curves 29

2.8 ROC Analysis When the True Diagnosis Is Not Binary 30

2.9 C-statistics and Other Measures to Compare Prediction Models 32

2.10 Detection and Localization of Multiple Lesions 33

2.11 Positive and Negative Predictive Values, Bayes' Theorem, and Case Study 2 35

2.11.1 Bayes' Theorem 36

2.12 Optimal Decision Threshold on the ROC Curve 38

2.12.1 Optimal Thresholds for Maximizing Classification 38

2.12.2 Optimal Threshold for Minimizing Cost 39

2.12.3 Optimal Decision Threshold: Rapid Eye Movement as a Marker for Depression Example 39

2.13 Interpreting the Results of Multiple Tests 40

2.13.1 Parallel Testing 40

2.13.2 Serial, or Sequential, Testing 41

3 Design of Diagnostic Accuracy Studies 45

3.1 Establish the Objective of the Study 45

3.2 Identify the Target Patient Population 49

3.3 Select a Sampling Plan for Patients 50

3.3.1 Phase I: Exploratory Studies 50

3.3.2 Phase II: Challenge Studies 50

3.3.3 Phase III: Clinical Studies 52

3.4 Select the Gold Standard 56

3.5 Choose a Measure of Accuracy 61

3.6 Identify Target Reader Population 63

3.7 Select Sampling Plan for Readers 64

3.8 Plan Data Collection 64

3.8.1 Format for Test Results 64

3.8.2 Data Collection for Reader Studies 65

3.8.3 Reader Training 71

3.9 Plan Data Analyses 72

3.9.1 Statistical Hypotheses 72

3.9.2 Planning for Covariate Adjustment 73

3.9.3 Reporting Test Results 75

3.10 Determine Sample Size 77

4 Estimation and Hypothesis Testing in a Single Sample 79

4.1 Binary-scale Data 80

4.1.1 Sensitivity and Specificity 80

4.1.2 Predictive Value of a Positive or Negative 82

4.1.3 Sensitivity, Specificity, and Predictive Values with Clustered Binary-scale Data 84

4.1.4 Likelihood Ratio 8...