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E-grāmata: Statistical Methods in Diagnostic Medicine

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(Virginia Commonwealth University, Richmond, VA), (The Cleveland Clinic Foundation, Clevelad, OH), (University of Washington, Seattle, WA)
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Statistical Methods in Diagnostic MedicinePalielināt
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"Praise for the First Edition " . . . the book is a valuable addition to the literature in the field, serving as a much-needed guide for both clinicians and advanced students."--Zentralblatt MATH A new edition of the cutting-edge guide to diagnostic tests in medical research In recent years, a considerable amount of research has focused on evolving methods for designing and analyzing diagnostic accuracy studies. Statistical Methods in Diagnostic Medicine, Second Edition continues to provide a comprehensive approach to the topic, guiding readers through the necessary practices for understanding these studies and generalizing the results to patient populations. Following a basic introduction to measuring test accuracy and study design, the authors successfully define various measures of diagnostic accuracy, describe strategies for designing diagnostic accuracy studies, and present key statistical methods for estimating and comparing test accuracy. Topics new to the Second Edition include: Methods for tests designed to detect and locate lesions Recommendations for covariate-adjustment Methods for estimating and comparing predictive values and sample sSample size calculation for multiple reader studies when pilot data are available Updated meta-analysis methods, now incorporating random effectsThree case studies thoroughly showcase some of the questions and statistical issues that arise in diagnostic medicine, with all associated data provided in detailed appendices. A related web site features Fortran, SAS(r), and R software packages so that readers can conduct their own analyses. Statistical Methods in Diagnostic Medicine, Second Edition is an excellent supplement for biostatistics courses at the graduate level. It also serves as a valuable reference for clinicians and researchers working in the fields of medicine, epidemiology, and biostatistics"--Provided by publisher.

This book provides a comprehensive account of statistical methods for the design and analysis of diagnostic studies, including sample size calculations, estimation of the accuracy of a diagnostic test, comparison of accuracies of competing diagnostic tests, and regression analysis of diagnostic accuracy data. Discussing recently developed methods for correction of verification bias and imperfect reference bias, methods for analysis of clustered diagnostic accuracy data, and meta-analysis methods, the authors stress common measures of diagnostic accuracy and designs for diagnostic accuracy studies, methods of estimation and hypothesis testing of the accuracy of diagnostic tests, and advanced analytic techniques such as methods for comparing correlated ROC curves in multi-reader studies, correcting verification bias, and correcting imperfect gold standards. The book has rapidly become a valuable addition to the literature of the field, serving as a much-needed guide for both clinicians and advanced students.

Recenzijas

"The authors, overall, have done a good job of revising their first edition, addressing the critical reviews as well as expanding and updating their coverage . . . In summary, this is a good book, focusing on medical diagnosis as the name promises, presenting a wealth of methods in detail with good discussion." (Journal of Biopharmaceutical Statistics, 2011) "Early chapters are accessible to readers with a basic knowledge of statistical and medical terminology, and the second section addresses data analysts with basic training in biostatistics. Later chapters assume deeper background in statistics, but the examples should be accessible to all. The 2002 edition has been updated throughout, and three new case studies have been added." (Booknews, 1 June 2011)

List of Figures xix
List of Tables xxiii
0.1 Preface
xxix
0.2 Acknowledgements
xxx
Part I Basic Concepts and Methods
1 Introduction
3(10)
1.1 Diagnostic Test Accuracy Studies
3(3)
1.2 Case Studies
6(4)
1.2.1 Case Study 1: Parathyroid Disease
6(1)
1.2.2 Case Study 2: Colon Cancer Detection
7(2)
1.2.3 Case Study 3: Carotid Artery Stenosis
9(1)
1.3 Software
10(1)
1.4 Topics Not Covered in This Book
10(3)
2 Measures of Diagnostic Accuracy
13(44)
2.1 Sensitivity and Specificity
14(7)
2.1.1 Basic Measures of Test Accuracy: Case Study 2
16(1)
2.1.2 Diagnostic Tests with Continuous Results: The Artificial Heart Valve Example
17(2)
2.1.3 Diagnostic Tests with Ordinal Results: Case Study 1
19(1)
2.1.4 Effect of Prevalence and Spectrum of Disease
19(2)
2.1.5 Analogy to a and Q Statistical Errors
21(1)
2.2 Combined Measures of Sensitivity and Specificity
21(3)
2.2.1 Problems Comparing Two or More Tests: Case Study 1
21(1)
2.2.2 Probability of a Correct Test Result
21(2)
2.2.3 Odds Ratio and Youden's Index
23(1)
2.3 Receiver Operating Characteristic (ROC) Curve
24(3)
2.3.1 ROC Curves: Artificial Heart Valve and Case Study 1
24(1)
2.3.2 ROC Curve Assumption
25(1)
2.3.3 Smooth, Fitted ROC Curves
26(1)
2.3.4 Advantages of ROC Curves
27(1)
2.4 Area Under the ROC Curve
27(7)
2.4.1 Interpretation of the Area Tinder the ROC Curve
28(1)
2.4.2 Magnitudes of the Area Under the ROC Curve
29(1)
2.4.3 Area Under the ROC Curve: Case Study 1
29(3)
2.4.4 Misinterpretations of the Area Under the ROC Curve
32(2)
2.5 Sensitivity at Fixed FPR
34(1)
2.6 Partial Area Under the ROC Curve
35(1)
2.7 Likelihood Ratios
36(5)
2.7.1 Three Examples to Illustrate Likelihood Ratios
37(2)
2.7.2 Limitations of Likelihood Ratios
39(1)
2.7.3 Proper and Improper ROC Curves
39(2)
2.8 ROC Analysis When the True Diagnosis Is Not Binary
41(2)
2.9 C-Statistics and Other Measures to Compare Prediction Models
43(1)
2.10 Detection and Localization of Multiple Lesions
44(3)
2.11 Positive and Negative Predictive Values, Bayes Theorem, and Case Study 2
47(4)
2.11.1 Bayes Theorem
48(3)
2.12 Optimal Decision Threshold on the ROC Curve
51(3)
2.12.1 Optimal Thresholds for Maximizing Classification
51(1)
2.12.2 Optimal Threshold for Minimizing Cost
52(1)
2.12.3 Optimal Decision Threshold: Rapid Eye Movement as a Marker for Depression Example
53(1)
2.13 Interpreting the Results of Multiple Tests
54(3)
2.13.1 Parallel Testing
54(1)
2.13.2 Serial, or Sequential, Testing
54(3)
3 Design of Diagnostic Accuracy Studies
57(46)
3.1 Establish the Objective of the Study
58(5)
3.2 Identify the Target Patient Population
63(1)
3.3 Select a Sampling Plan for Patients
64(8)
3.3.1 Phase I: Exploratory Studies
64(1)
3.3.2 Phase II: Challenge Studies
65(2)
3.3.3 Phase III: Clinical Studies
67(5)
3.4 Select the Gold Standard
72(7)
3.5 Choose A Measure of Accuracy
79(3)
3.6 Identify Target Reader Population
82(1)
3.7 Select Sampling Plan for Readers
83(1)
3.8 Plan Data Collection
84(10)
3.8.1 Format for Test Results
84(1)
3.8.2 Data Collection for Reader Studies
85(8)
3.8.3 Reader Training
93(1)
3.9 Plan Data Analyses
94(7)
3.9.1 Statistical Hypotheses
94(2)
3.9.2 Planning for Covariate Adjustment
96(2)
3.9.3 Reporting Test Results
98(3)
3.10 Determine Sample Size
101(2)
4 Estimation and Hypothesis Testing in a Single Sample
103(62)
4.1 Binary-Scale Data
104(13)
4.1.1 Sensitivity and Specificity
104(3)
4.1.2 Predictive Value of a Positive or Negative
107(3)
4.1.3 Sensitivity, Specificity and Predictive Values with Clustered Binary-Scale Data
110(1)
4.1.4 Likelihood Ratio (LR)
111(3)
4.1.5 Odds Ratio
114(3)
4.2 Ordinal-Scale Data
117(24)
4.2.1 Empirical ROC Curve
117(1)
4.2.2 Fitting a Smooth Curve
118(6)
4.2.3 Estimation of Sensitivity at a Particular False Positive Rate
124(4)
4.2.4 Area and Partial Area under the ROC Curve (Parametric Methods)
128(2)
4.2.5 Confidence Interval Estimation
130(3)
4.2.6 Area and Partial Area Under the ROC Curve (Nonparametric Methods)
133(4)
4.2.7 Nonparametric Analysis of Clustered Data.
137(2)
4.2.8 Degenerate Data
139(2)
4.2.9 Choosing Between Parametric, Semi-parametric and Nonparametric Methods
141(1)
4.3 Continuous-Scale Data
141(22)
4.3.1 Empirical ROC Curve
143(1)
4.3.2 Fitting a Smooth ROC Curve - Parametric, Semi-parametric and Nonparametric Methods
143(6)
4.3.3 Confidence Bands Around the Estimated ROC Curve
149(1)
4.3.4 Area and Partial Area Under the ROC Curve - Parametric, Nonparametric and Semi-parametric Methods
150(2)
4.3.5 Confidence Intervals for the Area Under the ROC Curve
152(2)
4.3.6 Fixed False Positive Rate - Sensitivity and the Decision Threshold
154(4)
4.3.7 Choosing the Optimal Operating Point and Decision Threshold
158(4)
4.3.8 Choosing between Parametric, Semi-parametric and Nonparametric Methods
162(1)
4.4 Testing the Hypothesis that the ROC Curve Area or Partial Area Is a Specific Value
163(2)
4.4.1 Testing Whether MRA has Any Ability to Detect Significant Carotid Stenosis
164(1)
5 Comparing the Accuracy of Two Diagnostic Tests
165(28)
5.1 Binary-Scale Data
166(8)
5.1.1 Sensitivity and Specificity
166(3)
5.1.2 Sensitivity and Specificity of Clustered Binary Data
169(2)
5.1.3 Predictive Probability of a Positive or Negative
171(3)
5.2 Ordinal- and Continuous-Scale Data
174(15)
5.2.1 Testing the Equality of Two ROC Curves
175(2)
5.2.2 Comparing ROC Curves at a Particular Point
177(3)
5.2.3 Determining the Range of FPRs for which TPRs Differ
180(2)
5.2.4 Comparison of the Area or Partial Area
182(7)
5.3 Tests of Equivalence
189(4)
5.3.1 Testing Whether ROC Curve Areas are Equivalent: Case Study 3
191(2)
6 Sample Size Calculations
193(38)
6.1 Studies Estimating the Accuracy of a Single Test
194(9)
6.1.1 Sample Size Calculations for Estimating Sensitivity and/or Specificity - Case Study
194(2)
6.1.2 Sample Size for Estimating the Area Under the ROC Curve - Case Study 2
196(2)
6.1.3 Studies with Clustered Data
198(1)
6.1.4 Testing the Hypothesis that the ROC Area is Equal to a Particular Value
199(1)
6.1.5 Sample Size for Estimating Sensitivity at Fixed FPR - Case Study 2
200(2)
6.1.6 Sample Size for Estimating the Partial Area Under the ROC Curve - Case Study 2
202(1)
6.2 Sample Size for Detecting a Difference in Accuracies of Two Tests
203(11)
6.2.1 Sample Size Software
204(1)
6.2.2 Sample Size for Comparing Tests' Sensitivity and/or Specificity - Case Study 1
204(2)
6.2.3 Sample Size for Comparing Tests' Positive and Negative Predictive Values - Case Study 1
206(2)
6.2.4 Sample Size for Comparing Tests' Area Under the ROC Curve - Case Study 2
208(1)
6.2.5 Sample Size for Comparing Tests with Clustered Data
209(2)
6.2.6 Sample Size for Comparing Tests' Sensitivity at Fixed FPR - Case Study 2
211(1)
6.2.7 Sample Size for Comparing Tests' Partial Area Under the ROC Curve - Case Study 2
212(2)
6.3 Sample Size for Assessing Non-Inferiority or Equivalency of Two Tests
214(4)
6.4 Sample Size for Determining a Suitable Cutoff Value
218(1)
6.5 Sample Size Determination for Multi-Reader Studies
219(9)
6.5.1 MRIVIC Sample Size Software
220(1)
6.5.2 MRMC Sample Size Calculations with No Pilot Data
220(6)
6.5.3 MRMC Sample Size Calculations with Pilot Data
226(2)
6.6 Alternative to Sample Size Formulae
228(3)
7 Introduction to Meta-analysis for Diagnostic Accuracy Studies
231(32)
7.1 Objectives
232(1)
7.2 Retrieval of the Literature
233(4)
7.2.1 Literature Search: Meta-analysis of Ultrasound for PAD
237(1)
7.3 Inclusion/Exclusion Criteria
237(4)
7.3.1 Inclusion/Exclusion Criteria: Meta-analysis of Ultrasound for PAD
240(1)
7.4 Extracting Information from the Literature
241(2)
7.4.1 Data Abstraction: Meta-analysis of Ultrasound for PAD
243(1)
7.5 Statistical Analysis
243(15)
7.5.1 Binary-Scale Data
243(1)
7.5.2 Ordinal- or Continuous- Scale Data
244(12)
7.5.3 Area Under the ROC Curve
256(2)
7.5.4 Other Methods
258(1)
7.6 Public Presentation
258(5)
7.6.1 Presentation of Results: Meta-analysis of Ultrasound for PAD
260(3)
Part II Advanced Methods
8 Regression Analysis for Independent ROC Data
263(34)
8.1 Four Clinical Studies
264(3)
8.1.1 Surgical Lesion in a Carotid Vessel Example
265(1)
8.1.2 Pancreatic Cancer Example
265(1)
8.1.3 Hearing Test Example
265(1)
8.1.4 Staging of Prostate Cancer Example
266(1)
8.2 Regression Models for Continuous-Scale Tests
267(20)
8.2.1 Indirect Regression Models for ROC Curves
268(4)
8.2.2 Direct Regression Models for ROC Curves
272(15)
8.3 Regression Models for Ordinal-Scale Tests
287(7)
8.3.1 Indirect Regression Models for Latent Smooth ROC Curves
288(3)
8.3.2 Direct Regression Model for Latent Smooth ROC Curves
291(1)
8.3.3 Detection of Periprostatic Invasion with Ultrasound
292(2)
8.4 Covariate Adjusted ROC Curves of Continuous-Scale tests
294(3)
9 Analysis of Multiple Reader and/or Multiple Test Studies
297(32)
9.1 Studies Comparing Multiple Tests with Covariates
298(12)
9.1.1 Two Clinical Studies
298(1)
9.1.2 Indirect Regression Models for Ordinal-Scale Tests
299(6)
9.1.3 Direct Regression Models for Continuous-scale Tests
305(5)
9.2 Studies with Multiple Readers and Multiple Tests
310(15)
9.2.1 Three MRMC Studies
310(1)
9.2.2 Statistical Methods for Analyzing MRMC Studies
311(12)
9.2.3 Analysis of the Interstitial Disease Example
323(1)
9.2.4 Comparisons between MRMC Methods
324(1)
9.3 Analysis of Multiple Tests Designed to Locate and Diagnose Lesions
325(4)
9.3.1 LROC Approach
326(1)
9.3.2 FROC Approach
326(1)
9.3.3 ROI Approach
327(2)
10 Methods for Correcting Verification Bias
329(60)
10.1 Examples
330(3)
10.1.1 Hepatic Scintigraph
331(1)
10.1.2 Screening Tests for Dementia Disorder Example
331(1)
10.1.3 Fever of Uncertain Origin
332(1)
10.1.4 CT and MRI for Staging Pancreatic Cancer Example
332(1)
10.1.5 NACC MDS on Alzheimer Disease (AD)
332(1)
10.2 Impact of Verification Bias
333(1)
10.3 A Single Binary-Scale Test
334(7)
10.3.1 Correction Methods Under the MAR Assumption
334(3)
10.3.2 Correction Methods Without the MAR Assumption
337(2)
10.3.3 Analysis of Hepatic Scintigraph Example, Continued
339(2)
10.4 Correlated Binary-Scale Tests
341(7)
10.4.1 ML Approach Without Any Covariates
341(3)
10.4.2 Analysis of Two Screening Tests for Dementia Disorder Example
344(1)
10.4.3 ML Approach With Covariates
344(3)
10.4.4 Analysis of Two Screening Tests for Dementia Disorder Example, Continued
347(1)
10.5 A Single Ordinal-Scale Test
348(12)
10.5.1 ML Approach Without Covariates
348(4)
10.5.2 Analysis of Fever of Uncertain Origin Example
352(2)
10.5.3 ML Approach With Covariates
354(3)
10.5.4 Analysis of New Screening Test for Dementia Disorder
357(3)
10.6 Correlated Ordinal-Scale Tests
360(12)
10.6.1 Weighted Estimating Equation Approaches for Latent Smooth ROC Curves
361(8)
10.6.2 Likelihood-Based Approach for ROC Areas
369(2)
10.6.3 Analysis of CT and MRI for Staging Pancreatic Cancer
371(1)
10.7 Continuous-Scale Tests
372(17)
10.7.1 Estimation of ROC Curves and Their Areas Under the MAR Assumption
374(7)
10.7.2 Estimation of ROC Curves and Areas under a Non-MAR Process
381(8)
11 Methods for Correcting Imperfect Gold Standard Bias
389(46)
11.1 Examples
390(3)
11.1.1 Binary Stool Test for Strongyloides Infection
391(1)
11.1.2 Binary Tine Test for Tuberculosis
391(1)
11.1.3 Binary-Scale X-rays for Pleural Thickening
391(1)
11.1.4 Bioassays for HIV
391(1)
11.1.5 Ordinal-Scale Evaluation by Pathologists for Detecting Carcinoma in Situ of the Uterine Cervix
392(1)
11.1.6 Ordinal-Scale and Continuous-Scale MRA for Carotid Artery Stenosis
392(1)
11.2 Impact of Imperfect Gold Standard Bias
393(2)
11.3 One Single Binary test in a Single Population
395(7)
11.3.1 Conditions for Model Identifiability
396(1)
11.3.2 The Frequentist-Based ML Method Under an Identifiable Model
397(1)
11.3.3 Bayesian Methods Under a Non-Identifiable Model
398(2)
11.3.4 Analysis of Strongyloides Infection Example
400(2)
11.4 One Single Binary test in G Populations
402(6)
11.4.1 Estimation Methods
403(3)
11.4.2 Tuberculosis Example
406(2)
11.5 Multiple Binary Tests in One Single Population
408(15)
11.5.1 Checking for Model Identifiability
408(1)
11.5.2 ML Estimates under the CIA
409(1)
11.5.3 Assessment of Pleural Thickening Example
410(1)
11.5.4 ML Approaches Under Identifiable Conditional Dependence Models
411(5)
11.5.5 Bioassays for HIV Example
416(5)
11.5.6 Bayesian Methods Under Conditional Dependence Models
421(1)
11.5.7 Analysis of the MRA for Carotid Stenosis Example
421(2)
11.6 Multiple Binary Tests in G Populations
423(2)
11.6.1 ML Approaches Under the CIA
423(1)
11.6.2 ML Approach Without the CIA Assumption
424(1)
11.7 Multiple Ordinal-Scale Tests in One Single Population
425(4)
11.7.1 Non-Parametric Estimation of ROC Curves Under the CIA
425(2)
11.7.2 Estimation of ROC Curves Under Some Conditional Dependence Models
427(1)
11.7.3 Analysis of Ordinal-Scale Tests for Detecting Carcinoma in Situ of the Uterine Cervix
428(1)
11.8 Multiple-Scale Tests in One Single Population
429(6)
11.8.1 Re-Analysis of the Accuracy of Continuous-Scale MRA for Detection of Significant Carotid Stenosis
433(2)
12 Statistical Analysis for Meta-analysis
435(14)
12.1 Binary-Scale Data
436(2)
12.1.1 Random Effects Model: Meta-analysis of Ultrasound for PAD
437(1)
12.2 Ordinal- or Continuous-Scale Data
438(7)
12.2.1 Random Effects Model
438(1)
12.2.2 Bivariate Approach
439(2)
12.2.3 Binary Regression Model
441(2)
12.2.4 Hierarchical SROC (HSROC) Curve
443(2)
12.2.5 Other Methods
445(1)
12.3 ROC Curve Area
445(4)
12.3.1 Empirical Bayes Method: Meta-analysis of DST
448(1)
Appendix A: Case Studies and
Chapter 8 Data		 449(28)
Appendix B: Jackknife and Bootstrap Methods of Estimating Variances and Confidence Intervals 477
Xiao-Hua Zhou, PhD, is Professor of Biostatistics at the University of Washington and Director and Research Career Scientist at the Biostatistics Unit of the Veterans Affairs Puget Sound Healthcare System. He is a Fellow of the American Statistical Association and the author of more than 100 published articles on statistical methods in diagnostic medicine and causal inferences. Nancy A. Obuchowski, PhD, is Vice Chairperson of the Department of Quantitative Health Sciences at the Cleveland Clinic Foundation. A Fellow of the American Statistical Association, she has written more than 100 journal articles on the design and analysis of studies of screening and diagnostic tests.

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.
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