Zhou / Obuchowski / McClish Statistical Methods in Diagnostic Medicine

2. Auflage 2011
ISBN: 978-0-470-90650-7
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 592 Seiten, E-Book

Reihe: Wiley Series in Probability and Statistics

Praise for the First Edition
" . . . the book is a valuable addition to the literature in thefield, serving as a much-needed guide for both clinicians andadvanced students."--Zentralblatt MATH
A new edition of the cutting-edge guide to diagnostic tests inmedical research
In recent years, a considerable amount of research has focusedon evolving methods for designing and analyzing diagnostic accuracystudies. Statistical Methods in Diagnostic Medicine, Second Editioncontinues to provide a comprehensive approach to the topic, guidingreaders through the necessary practices for understanding thesestudies and generalizing the results to patient populations.
Following a basic introduction to measuring test accuracy andstudy design, the authors successfully define various measures ofdiagnostic accuracy, describe strategies for designing diagnosticaccuracy studies, and present key statistical methods forestimating and comparing test accuracy. Topics new to the SecondEdition include:
* Methods for tests designed to detect and locate lesions
* Recommendations for covariate-adjustment
* Methods for estimating and comparing predictive values andsample size calculations
* Correcting techniques for verification and imperfect standardbiases
* Sample size calculation for multiple reader studies when pilotdata are available
* Updated meta-analysis methods, now incorporating randomeffects
Three case studies thoroughly showcase some of the questions andstatistical issues that arise in diagnostic medicine, with allassociated data provided in detailed appendices. A related web sitefeatures Fortran, SAS®, and R software packages so thatreaders can conduct their own analyses.
Statistical Methods in Diagnostic Medicine, Second Edition is anexcellent supplement for biostatistics courses at the graduatelevel. It also serves as a valuable reference for clinicians andresearchers working in the fields of medicine, epidemiology, andbiostatistics.

Autoren/Hrsg.

Zhou, Xiao-Hua

Obuchowski, Nancy A.

McClish, Donna K.

Inhaltsverzeichnis

List of Figures.
List of Tables.
Preface.
Acknowledgments.
Part 1. Basic Concepts and Methods.
1. Introduction.
1.1 Diagnostic Test Accuracy Studies.
1.2 Case Studies.
1.3 Software.
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®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.
7.1 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.
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.
10.1 Examples.
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.
11.1 Examples.
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

Über Autor(innen)

Xiao-Hua Zhou, PhD, is Professor of Biostatistics at theUniversity of Washington and Director and Research Career Scientistat the Biostatistics Unit of the Veterans Affairs Puget SoundHealthcare System. He is a Fellow of the American StatisticalAssociation and the author of more than 100 published articles onstatistical methods in diagnostic medicine and causal inferences.
Nancy A. Obuchowski, PhD, is Vice Chairperson of theDepartment of Quantitative Health Sciences at the Cleveland ClinicFoundation. A Fellow of the American Statistical Association, shehas written more than 100 journal articles on the design andanalysis of studies of screening and diagnostic tests.
Donna K. McClish, PhD, is Associate Professor andGraduate Program Director in Biostatistics at Virginia CommonwealthUniversity. She has written more than 100 journal articles onstatistical methods in epidemiology, diagnostic medicine, andhealth services research.