Statistical Methods in Diagnostic Medicine
Wiley Series in Probability and Statistics
2. Auflage April 2011
592 Seiten, Hardcover
Wiley & Sons Ltd
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 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. This updated edition features edited case studies and new methods, including screening tests, semi-parametric and non-parametric regression models, and SROC and partial areas. Clinicians and advanced students will benefit from this much-needed guide.
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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 size calculations
* Correcting techniques for verification and imperfect standard biases
* Sample size calculation for multiple reader studies when pilot data are available
* Updated meta-analysis methods, now incorporating random effects
Three 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.
List of Tables.
Part 1. Basic Concepts and Methods.
1.1 Diagnostic Test Accuracy Studies.
1.2 Case Studies.
1.4 Topics Not Covered in This Book.
2. Measures of Diagnostic Accuracy.
2.1 Sensitivity and Specificity.
2.2 Combined Measures of Sensitivity and Specificity.
2.3 Receiver Operating Characteristic (ROC) Curve.
2.4 Area Under the ROC Curves.
2.5 Sensitivity at Fixed FPR.
2.6 Partial Area Under the ROC Curve.
2.7 Likelihood Ratios.
2.8 ROC Analysis When the True Diagnosis is Not Binary.
2.9 C-Statistic and Other Measures to Compare Prediction Models.
2.10 Localization and Detection of Multiple Lesions.
2.11 Positive and Negative Predictive Values, Bayes Theorem, and Case Study 2.
2.12 Optimal Decision Threshold on the ROC Curve.
2.13 Interpreting the Results of Multiple Tests.
3. Design of Diagnostic Accuracy Studies.
3.1 Establish the Objective of the Study.
3.2 Identify the Target Patient Population.
3.3 Select a Sampling Plan for Patients.
3.4 Select the Gold Standard.
3.5 Choose a Measure of Accuracy.
3.6 Identify Target Reader Population.
3.7 Select Sampling Plan for Readers.
3.8 Plan Data Collection.
3.9 Plan Data Analyses.
3.10 Determine Sample Size.
4. Estimation and Hypothesis Testing in a Single Sample.
4.1 Binary-Scale Data.
4.2 Original-Scale Data.
4.3 Continuous-Scale Data.
4.4 Testing the Hypothesis that the ROC Curve Area or Partial Area is a Specific Value.
5. Comparing the Accuracy of Two Diagnostic Tests.
5.1 Binary-Scale Data.
5.2 Original- and Continuous-Scale Data.
5.3 Tests of Equivalence.
6. Sample Size Calculations.
6.1 Studies Estimating the Accuracy of a Single Test.
6.2 Sample Size for Detecting a Di(r)erence in Accuracies of Two Tests.
6.3 Sample Size for Assessing Non-Inferiority or Equivalency of Two Tests.
6.4 Sample Size for Determining a Suitable Cutoff Value.
6.5 Sample Size Determining for Multi-Reader Studies.
6.6 Alternative to Sample Size Formulae.
7. Issues in Meta-analysis for Diagnostic Accuracy Studies.
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.
Part 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 Multiple Reader and/or Multiple Test Studies.
9.1 Studies Comparing Multiple Tests with Covariates.
9.2 Studies with Multiple Reader and Multiple Tests.
9.3 Analysis of Multiple Tests Designed to Locate and Diagnose Lesions.
10. Methods for Correcting Verification Bias.
10.2 Impact of Verification Bias.
10.3 A Single Binary-Scale Test.
10.4 Correlated Binary-Scale Tests.
10.5 A Single Ordinal-Scale Test.
10.6 Correlated Ordinal-Scale Tests.
10.7 Continuous-Scale Tests.
11. Methods for Correcting Imperfect Gold Standard Bias.
11.2 Impact of Imperfect Gold Standard Bias.
11.3 One Single Binary Test in a Single Population.
11.4 One Single Binary Test in G Populations.
11.5 Multiple Binary Tests in One Single Population.
11.6 Multiple Binary Tests in G Populations.
11.7 Multiple Ordinal-Scale Tests in One Single Population.
11.8 Multiple Tests in One Single Population.
12. Statistical Analysis for Meta-analysis.
12.1 Binary-Scale Data.
12.2 Ordinal-or Continuous-Scale Data.
12.3 ROC Curve Area.
Appendix A: Case Studies and Chapter 8 Data.
Appendix B: Jackknife and Bootstrap Methods of Estimating Variances and Confidence Intervals. Nam
"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)
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.