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|a Statistical methods in diagnostic medicine /
|c Xiao-Hua Zhou, Nancy A. Obuchowski, Donna K. McClish.
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|a 2nd ed.
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|a Basic Concepts and Methods. Introduction -- Measures of Diagnostic Accuracy -- Design of Diagnostic Accuracy Studies -- Estimation and Hypothesis Testing in a Single Sample -- Comparing the Accuracy of Two Diagnostic Tests -- Sample Size Calculations -- Introduction to Meta-analysis for Diagnostic Accuracy Studies -- Advanced Methods. Regression Analysis for Independent ROC Data -- Analysis of Multiple Reader and/or Multiple Test Studies -- Methods for Correcting Verification Bias -- Methods for Correcting Imperfect Gold Standard Bias -- Statistical Analysis for Meta-analysis -- Appendix A: Case Studies and Chapter 8 Data -- Appendix B: Jackknife and Bootstrap Methods of Estimating Variances and Confidence Intervals.
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|a Includes bibliographical references and index.
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|a "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
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|b 2nd ed.
|d Hoboken, N.J. : Wiley, ©2011
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|6 505-00/(S
|a Statistical Methods in Diagnostic Medicine -- CONTENTS -- List of Figures -- List of Tables -- 0.1 PREFACE -- 0.2 ACKNOWLEDGEMENTS -- PART I BASIC CONCEPTS AND METHODS -- 1 Introduction -- 1.1 Diagnostic Test Accuracy Studies -- 1.2 Case Studies -- 1.2.1 Case Study 1: Parathyroid Disease -- 1.2.2 Case Study 2: Colon Cancer Detection -- 1.2.3 Case Study 3: Carotid Artery Stenosis -- 1.3 Software -- 1.4 Topics Not Covered in This Book -- 2 Measures of Diagnostic Accuracy -- 2.1 Sensitivity and Specificity -- 2.1.1 Basic Measures of Test Accuracy: Case Study 2 -- 2.1.2 Diagnostic Tests with Continuous Results: The Artificial Heart Valve Example -- 2.1.3 Diagnostic Tests with Ordinal Results: Case Study 1 -- 2.1.4 Effect of Prevalence and Spectrum of Disease -- 2.1.5 Analogy to α and β Statistical Errors -- 2.2 Combined Measures of Sensitivity and Specificity -- 2.2.1 Problems Comparing Two or More Tests: Case Study 1 -- 2.2.2 Probability of a Correct Test Result -- 2.2.3 Odds Ratio and Youden's Index -- 2.3 Receiver Operating Characteristic (ROC) Curve -- 2.3.1 ROC Curves: Artificial Heart Valve and Case Study 1 -- 2.3.2 ROC Curve Assumption -- 2.3.3 Smooth, Fitted ROC Curves -- 2.3.4 Advantages of ROC Curves -- 2.4 Area Under the ROC Curve -- 2.4.1 Interpretation of the Area Under the ROC Curve -- 2.4.2 Magnitudes of the Area Under the ROC Curve -- 2.4.3 Area Under the ROC Curve: Case Study 1 -- 2.4.4 Misinterpretations of the Area Under the ROC Curve -- 2.5 Sensitivity at Fixed FPR -- 2.6 Partial Area Under the ROC Curve -- 2.7 Likelihood Ratios -- 2.7.1 Three Examples to Illustrate Likelihood Ratios -- 2.7.2 Limitations of Likelihood Ratios -- 2.7.3 Proper and Improper ROC Curves -- 2.8 ROC Analysis When the True Diagnosis Is Not Binary -- 2.9 C-Statistics and Other Measures to Compare Prediction Models.
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