Statistical Methods in Diagnostic Medicine

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Xiao-Hua Zhou (Peking University, Beijing, China) Jiarui Sun (Peking University, Beijing, China)

Statistical Methods in Diagnostic Medicine

Name: Statistical Methods in Diagnostic Medicine
Price: 248.95 AUD
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ISBN: 9781394220212

Xiao-Hua Zhou (Peking University, Beijing, China), Jiarui Sun (Peking University, Beijing, China), Gene A. Pennello (U.S. Food and Drug Administration (FDA), Silver Spring, MD) , Nancy A. Obuchowski (The Cleveland Clinic Foundation, OH)

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English

John Wiley & Sons Inc
26 March 2026

Medical research; Epidemiology & medical statistics; Medical diagnosis; Probability & statistics

Series: Wiley Series in Probability and Statistics

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.

By: Xiao-Hua Zhou (Peking University Beijing China), Jiarui Sun (Peking University, Beijing, China), Gene A. Pennello (U.S. Food and Drug Administration (FDA), Silver Spring, MD), Nancy A. Obuchowski (The Cleveland Clinic Foundation, OH), Donna K. McClish (Medical College of Virginia)
Imprint: John Wiley & Sons Inc
Country of Publication: United States
Edition: 3rd edition
ISBN: 9781394220212
ISBN 10: 1394220219
Series: Wiley Series in Probability and Statistics
Pages: 576
Publication Date: 26 March 2026
Audience: Professional and scholarly , College/higher education , Undergraduate , Further / Higher Education
Format: Hardback
Publisher's Status: Active

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 1.3 Software 8 1.4 Topics Not Covered in This Book 8 2 Measures of Diagnostic Accuracy 9 2.1 Sensitivity and Specificity 9 2.2 Combined Measures of Sensitivity and Specificity 15 2.3 ROC Curve 17 2.4 Area Under the ROC Curve 20 2.5 Sensitivity at Fixed FPR 25 2.6 Partial Area Under the ROC Curve 25 2.7 Likelihood Ratios 26 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 2.12 Optimal Decision Threshold on the ROC Curve 38 2.13 Interpreting the Results of Multiple Tests 40 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 3.4 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 3.9 Plan Data Analyses 72 3.10 Determine Sample Size 77 4 Estimation and Hypothesis Testing in a Single Sample 79 4.1 Binary-scale Data 80 4.2 Ordinal-scale Data 89 4.3 Continuous-scale Data 108 4.4 Testing the Hypothesis that the ROC Curve Area or Partial Area Is a Specific Value 126 5 Comparing the Accuracy of Two Diagnostic Tests 129 5.1 Binary-scale Data 130 5.2 Ordinal- and Continuous-scale Data 136 5.3 Tests of Equivalence 148 6 Sample Size Calculations 153 6.1 Studies Estimating the Accuracy of a Single Test 153 6.2 Sample Size for Detecting a Difference in Accuracies of Two Tests 161 6.3 Sample Size for Assessing Non-inferiority or Equivalency of Two Tests 169 6.4 Sample Size for Determining a Suitable Cutoff Value 172 6.5 Sample Size Determination for Multi-reader Studies 173 6.6 Alternative to Sample Size Formulae 180 7 Introduction to Meta-analysis for Diagnostic Accuracy Studies 181 7.1 Objectives 182 7.2 Retrieval of the Literature 182 7.3 Inclusion/Exclusion Criteria 186 7.4 Extracting Information from the Literature 188 7.5 Statistical Analysis 190 7.6 Public Presentation 202 Part II Advanced Methods 205 8 Regression Analysis for Independent ROC Data 207 8.1 Four Clinical Studies 208 8.2 Regression Models for Continuous-scale Tests 210 8.3 Regression Models for Ordinal-scale Tests 228 8.4 Covariate AROC Curves of Continuous-scale Tests 233 9 Analysis of Multiple Reader and/or Multiple Test Studies 235 9.1 Studies Comparing Multiple Tests with Covariates 235 9.2 Studies with Multiple Readers and Multiple Tests 245 10 Methods for Correcting Verification Bias 257 10.1 Examples 258 10.2 Impact of Verification Bias 260 10.3 A Single Binary-scale Test 261 10.4 Correlated Binary-scale Tests 267 10.5 A Single Ordinal-scale Test 276 10.6 Correlated Ordinal-scale Tests 286 10.7 Continuous-scale Tests 296 11 Methods for Correcting Imperfect Gold Standard Bias 313 11.1 Examples 314 11.2 Impact of Imperfect Gold Standard Bias 315 11.3 One Single Binary Test in a Single Population 317 11.4 One Single Binary Test in G Populations 324 11.5 Multiple Binary Tests in One Single Population 329 11.6 Multiple Binary Tests in G Populations 341 11.7 Multiple Ordinal-scale Tests in One Single Population 343 11.8 Multiple-scale Tests in One Single Population 347 12 Location-specific ROC Methods for Diagnostic Imaging 353 12.1 Examples 353 12.2 LROC Approach 355 12.3 FROC Approach 360 12.4 ROI Approach 377 12.5 Comparison Between Location-specific ROC Methods 383 13 Technical Performance (""Accuracy"") of Quantitative Imaging Biomarkers 385 13.1 Quantitative Imaging Biomarkers 385 13.2 Technical Performance Characteristics of a QIB 387 13.3 Precision 389 13.4 Bias and Linearity 398 13.5 Other Metrics of QIB Performance 405 13.6 Clinical Performance 407 14 Medical Test Efficacy and Effectiveness 413 14.1 General Notation 414 14.2 Prognostic Effects 416 14.3 Predictive Effects 416 14.4 Test Strategies for Assigning Treatments 417 14.5 Explanatory Versus Pragmatic Trials of Tests 417 14.6 Explanatory Trial Designs 418 14.7 Pragmatic Trial Designs 420 14.8 Adaptive Treatment Strategy Trial Designs 423 14.9 Treatment Selection Tests 424 14.10 Follow-on Treatment Selection Tests 425 14.11 Bibliographic Notes 428 15 Statistical Analysis for Meta-analysis 433 15.1 Binary-scale Data 433 15.2 Ordinal- or Continuous-scale Data 435 15.3 ROC Curve Area 441 15.4 Publication Bias 443 16 Risk Prediction 449 16.1 Risk Calculators 451 16.2 Calibration 454 16.3 Discrimination 464 16.4 Appendix 16-A: Survival Analysis 469 16.5 Appendix 16-B: Survival Analysis for Competing Risks 475 16.6 Appendix 16-C: Stochastic Processes for Survival Analysis 480 16.7 Appendix 16-D: Bibliographic Notes 481 Appendix-A: Case Studies and Chapter 8 Data 485 Appendix-B: Jackknife and Bootstrap Methods of Estimating Variances and Confidence Intervals 513 Bibliography 517 Index 555

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.