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 α and β 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 86 4.1.
5 Odds Ratio 88 4.2 Ordinal-scale Data 89 4.2.1 Empirical ROC Curve 90 4.2.2 Fitting a Smooth Curve 90 4.2.3 Estimation of Sensitivity at a Particular FPR 95 4.
2.4 Area and Partial Area Under the ROC Curve (Parametric Methods) 97 4.2.5 ci Estimation 99 4.2.6 Area and Partial Area Under the ROC Curve (Nonparametric Methods) 102 4.2.7 Nonparametric Analysis of Clustered Data 105 4.
2.8 Degenerate Data 106 4.2.9 Choosing Between Parametric, Semi-parametric, and Nonparametric Methods 108 4.3 Continuous-scale Data 108 4.3.1 Empirical ROC Curve 109 4.3.
2 Fitting a Smooth ROC Curve - Parametric, Semi-parametric, and Nonparametric Methods 110 4.3.3 Confidence Bands Around the Estimated ROC Curve 115 4.3.4 Area and Partial Area Under the ROC Curve - Parametric, Nonparametric, and Semi-parametric Methods 116 4.3.5 CIs for the Area Under the ROC Curve 117 4.3.
6 Fixed FPR - Sensitivity and the Decision Threshold 119 4.3.7 Choosing the Optimal Operating Point and Decision Threshold 122 4.3.8 Choosing Between Parametric, Semi-parametric, and Nonparametric Methods 125 4.4 Testing the Hypothesis that the ROC Curve Area or Partial Area Is a Specific Value 126 4.4.1 Testing Whether MRA Has Any Ability to Detect Significant Carotid Stenosis 127 5 Comparing the Accuracy of Two Diagnostic Tests 129 5.
1 Binary-scale Data 130 5.1.1 Sensitivity and Specificity 130 5.1.2 Sensitivity and Specificity of Clustered Binary Data 132 5.1.3 Predictive Probability of a Positive or Negative 134 5.2 Ordinal- and Continuous-scale Data 136 5.
2.1 Testing the Equality of Two ROC Curves 137 5.2.2 Comparing ROC Curves at a Particular Point 140 5.2.3 Determining the Range of FPRs for Which TPRs Differ 141 5.2.4 Comparison of the Area or Partial Area 143 5.
3 Tests of Equivalence 148 5.3.1 Testing Whether ROC Curve Areas Are Equivalent: Case Study 3 150 6 Sample Size Calculations 153 6.1 Studies Estimating the Accuracy of a Single Test 153 6.1.1 Sample Size Calculations for Estimating Sensitivity and/or Specificity - Case Study 1 153 6.1.2 Sample Size for Estimating the Area Under the ROC Curve - Case Study 2 155 6.
1.3 Studies with Clustered Data 157 6.1.4 Testing the Hypothesis That the ROC Area Is Equal to a Particular Value 158 6.1.5 Sample Size for Estimating Sensitivity at Fixed FPR - Case Study 2 158 6.1.6 Sample Size for Estimating the Partial Area Under the ROC Curve - Case Study 2 160 6.
2 Sample Size for Detecting a Difference in Accuracies of Two Tests 161 6.2.1 Sample Size Software 161 6.2.2 Sample Size for Comparing Tests'' Sensitivity and/or Specificity - Case Study 1 161 6.2.3 Sample Size for Comparing Tests'' Positive and Negative Predictive Values - Case Study 1 163 6.2.
4 Sample Size for Comparing Tests'' Area Under the ROC Curve - Case Study 2 164 6.2.5 Sample Size for Comparing Tests with Clustered Data 165 6.2.6 Sample Size for Comparing Tests'' Sensitivity at Fixed FPR - Case Study 2 166 6.2.7 Sample Size for Comparing Tests'' Partial Area Under the ROC Curve - Case Study 2 167 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 6.5.1 MRMC Sample Size Software 173 6.5.2 MRMC Sample Size Calculations with No Pilot Data 174 6.5.3 MRMC Sample Size Calculations with Pilot Data 179 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 7.2.1 Literature Search: Meta-analysis of Ultrasound for PAD 186 7.3 Inclusion/Exclusion Criteria 186 7.3.1 Inclusion/Exclusion Criteria: Meta-analysis of Ultrasound for PAD 188 7.
4 Extracting Information from the Literature 188 7.4.1 Data Abstraction: Meta-analysis of Ultrasound for PAD 190 7.5 Statistical Analysis 190 7.5.1 Binary-scale Data 190 7.5.2 Ordinal- or Continuous-scale Data 191 7.
5.3 Area Under the ROC Curve 200 7.5.4 Other Methods 202 7.6 Public Presentation 202 7.6.1 Presentation of Results: Meta-analysis of Ultrasound for PAD 204 Part II Advanced Methods 205 8 Regression Analysis for Independent ROC Data 207 8.1 Four Clinical Studies 208 8.
1.1 Surgical Lesion in a Carotid Vessel Example 208 8.1.2 Pancreatic Cancer Example 208 8.1.3 Hearing Test Example 208 8.1.4 Staging of Prostate Cancer Example 209 8.
2 Regression Models for Continuous-scale Tests 210 8.2.1 Indirect Regression Models for ROC Curves 211 8.2.2 Direct Regression Models for ROC Curves 214 8.3 Regression Models for Ordinal-scale Tests 228 8.3.1 Indirect Regression Models for Latent Smooth ROC Curves 228 8.
3.2 Direct Regression Model for Latent Smooth ROC Curves 230 8.3.3 Detection of Periprostatic Invasion with Ultrasound 232 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 9.1.1 Two Clinical Studies 235 9.
1.2 Indirect Regression Models for Ordinal-scale Tests 236 9.1.3 Direct Regression Models for Continuous-scale Tests 241 9.2 Studies with Multiple Readers and Multiple Tests 245 9.2.1 Three MRMC Studies 245 9.2.
2 Statistical Methods for Analyzing MRMC Studies 246 9.2.3 Analysis of the Interstitial Disease Example 254 9.2.4 Comparisons Between MRMC Methods 254 10 Methods for Correcting Verification Bias 257 10.1 Examples 258 10.1.1 Hepatic Scintigraph 258 10.
1.2 Sc.