Xiao-Hua Zhou & Donna K. McClish 
Statistical Methods in Diagnostic Medicine 

An important role of diagnostic medicine research is to estimate and compare the accuracies of diagnostic tests. This book provides a comprehensive account of statistical methods for 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, Statistical Methods in Diagnostic Medicine explains:

* Common measures of diagnostic accuracy and designs for diagnostic accuracy studies

* Methods of estimation and hypothesis testing of the accuracy of diagnostic tests

* Meta-analysis

* Advanced analytic techniques-including methods for comparing correlated ROC curves in multi-reader studies, correcting verification bias, and correcting when an imperfect gold standard is used

Thoroughly detailed with numerous applications and end-of-chapter problems as well as a related FTP site providing FORTRAN program listings, data sets, and instructional hints, Statistical Methods in Diagnostic Medicine is a valuable addition to the literature of the field, serving as a much-needed guide for both clinicians and advanced students.

Mục lục

Preface.

Acknowledgments.

1. Introduction.

1.1 Why This Book?

1.2 What Is Diagnostic Accuracy?

1.3 Landmarks in Statistical Methods for Diagnostic Medicine.

1.4 Software.

1.5 Topics not Covered in This Book.

1.6 Summary.

I BASIC CONCEPTS AND METHODS.

2. Measures of Diagnostic Accuracy.

2.1 Sensitivity and Specificity.

2.2 The Combined Measures of Sensitivity and Specificity.

2.3 The ROC Curve.

2.4 The Area Under the ROC Curve.

2.5 The Sensitivity at a Fixed FPR.

2.6 The Partial Area Under the ROC Curve.

2.7 Likelihood Ratios.

2.8 Other ROC Curve Indices.

2.9 The Localization and Detection of Multiple Abnormalities.

2.10 Interpretation of Diagnostic Tests.

2.11 Optimal Decision Threshold on the ROC Curve.

2.12 Multiple Tests.

3. The Design of Diagnostic Accuracy Studies.

3.1 Determining the Objective of the Study.

3.2 Identifying the Target-Patient Population.

3.3 Selecting a Sampling Plan for Patients.

3.4 Selecting the Gold Standard.

3.5 Choosing a Measure of Accuracy.

3.6 Identifying the Target-Reader Population.

3.7 Selecting a Sampling Plan for Readers.

3.8 Planning the Data Collection.

3.9 Planning the Data Analyses.

3.10 Determining the Sample Size.

4. Estimation and Hypothesis Testing in a Single Sample.

4.1 Binary-Scale Data.

4.2 Ordinal-Scale Data.

4.3 Continuous-Scale Data.

4.4 Hypothesis Testing About the ROC Area.

5. Comparing the Accuracy of Two Diagnostic Tests.

5.1 Binary-Scale Data.

5.2 Ordinal- and Continuous-Scale Data.

5.3 Tests of Equivalence.

6. Sample Size Calculation.

6.1 The Sample Size for Accuracy Studies of a Single Test.

6.2 The Sample Size for the Accuracy of Two Tests.

6.3 The Sample Size for Equivalent Studies of Two Tests.

6.4 The Sample Size for Determining a Suitable Cutoff Value.

7. Issues in Meta-Analysis for Diagnostic Tests.

7.1 Objectives.

7.2 Retrieval of the Literature.

7.3 Inclusion-Exclusion Criteria.

7.4 Extracting Information From the Literature.

7.5 Statistical Analysis.

7.6 Public Presentation.

II ADVANCED METHODS.

8. Regression Analysis for Independent ROC Data.

8.1 Four Clinical Studies.

8.2 Regression Models for Continuous-Scale Tests.

8.3 Regression Models for Ordinal-Scale Tests.

9. Analysis of Correlated ROC Data.

9.1 Studies With Multiple Test Measurements of the Same Patient.

9.2 Studies With Multiple Readers and Tests.

9.3 Sample Size Calculation for Multireader Studies.

10. Methods for Correcting Verification Bias.

10.1 A Single Binary-Scale Test.

10.2 Correlated Binary-Scale Tests.

10.3 A Single Ordinal-Scale Test.

10.4 Correlated Ordinal-Scale Tests.

11. Methods for Correcting Imperfect Standard Bias.

11.1 One Single Test in a Single Population.

11.2 One Single Test in G Populations.

11.3 Multiple Tests in One Single Population.

11.4 Multiple Binary Tests in G Populations.

12. Statistical Methods for Meta-Analysis.

12.1 Sensitivity and Specificity Pairs.

12.2 ROC Curve Areas.

Index.

Giới thiệu về tác giả

XIAO-HUA ANDREW ZHOU is Associate Professor in the Department of Biostatistics at Indiana University’s School of Medicine.

NANCY A. OBUCHOWSKI is Associate Staff at the Cleveland Clinic Foundation.

DONNA K. Mc CLISH is Associate Professor of Biostatistics at Virginia Commonwealth University’s Medical College of Virginia.