Description
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.
Table of Contents
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
