Statistical Methods in Diagnostic Medicine.

By: Zhou, Xiao-HuaContributor(s): Obuchowski, Nancy A | McClish, Donna KMaterial type: TextTextSeries: Wiley Series in Probability and Statistics SerPublisher: Hoboken : John Wiley & Sons, Incorporated, 2014Copyright date: ©2011Edition: 2nd edDescription: 1 online resource (587 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9780470906507Subject(s): Medical statistics | Medicine -- Research -- Statistical methodsGenre/Form: Electronic books.Additional physical formats: Print version:: Statistical Methods in Diagnostic MedicineDDC classification: 610.72 LOC classification: R853.S7 -- S767 2011ebOnline resources: Click to View
Contents:
Intro -- 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.
2.10 Detection and Localization of Multiple Lesions -- 2.11 Positive and Negative Predictive Values, Bayes Theorem, and Case Study 2 -- 2.11.1 Bayes Theorem -- 2.12 Optimal Decision Threshold on the ROC Curve -- 2.12.1 Optimal Thresholds for Maximizing Classification -- 2.12.2 Optimal Threshold for Minimizing Cost -- 2.12.3 Optimal Decision Threshold: Rapid Eye Movement as a Marker for Depression Example -- 2.13 Interpreting the Results of Multiple Tests -- 2.13.1 Parallel Testing -- 2.13.2 Serial, or Sequential, Testing -- 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 -- 3.3.1 Phase I: Exploratory Studies -- 3.3.2 Phase II: Challenge Studies -- 3.3.3 Phase III: Clinical Studies -- 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 -- 3.8.1 Format for Test Results -- 3.8.2 Data Collection for Reader Studies -- 3.8.3 Reader Training -- 3.9 Plan Data Analyses -- 3.9.1 Statistical Hypotheses -- 3.9.2 Planning for Covariate Adjustment -- 3.9.3 Reporting Test Results -- 3.10 Determine Sample Size -- 4 Estimation and Hypothesis Testing in a Single Sample -- 4.1 Binary-Scale Data -- 4.1.1 Sensitivity and Specificity -- 4.1.2 Predictive Value of a Positive or Negative -- 4.1.3 Sensitivity, Specificity and Predictive Values with Clustered Binary-Scale Data -- 4.1.4 Likelihood Ratio (LR) -- 4.1.5 Odds Ratio -- 4.2 Ordinal-Scale Data -- 4.2.1 Empirical ROC Curve -- 4.2.2 Fitting a Smooth Curve -- 4.2.3 Estimation of Sensitivity at a Particular False Positive Rate -- 4.2.4 Area and Partial Area under the ROC Curve (Parametric Methods) -- 4.2.5 Confidence Interval Estimation.
4.2.6 Area and Partial Area Under the ROC Curve (Nonparametric Methods) -- 4.2.7 Nonparametric Analysis of Clustered Data. -- 4.2.8 Degenerate Data -- 4.2.9 Choosing Between Parametric, Semi-parametric and Nonparametric Methods -- 4.3 Continuous-Scale Data -- 4.3.1 Empirical ROC Curve -- 4.3.2 Fitting a Smooth ROC Curve - Parametric, Semi-parametric and Nonparametric Methods -- 4.3.3 Confidence Bands Around the Estimated ROC Curve -- 4.3.4 Area and Partial Area Under the ROC Curve - Parametric, Nonparametric and Semi-parametric Methods -- 4.3.5 Confidence Intervals for the Area Under the ROC Curve -- 4.3.6 Fixed False Positive Rate - Sensitivity and the Decision Threshold -- 4.3.7 Choosing the Optimal Operating Point and Decision Threshold -- 4.3.8 Choosing between Parametric, Semi-parametric and Nonparametric Methods -- 4.4 Testing the Hypothesis that the ROC Curve Area or Partial Area Is a Specific Value -- 4.4.1 Testing Whether MRA has Any Ability to Detect Significant Carotid Stenosis -- 5 Comparing the Accuracy of Two Diagnostic Tests -- 5.1 Binary-Scale Data -- 5.1.1 Sensitivity and Specificity -- 5.1.2 Sensitivity and Specificity of Clustered Binary Data -- 5.1.3 Predictive Probability of a Positive or Negative -- 5.2 Ordinal- and Continuous-Scale Data -- 5.2.1 Testing the Equality of Two ROC Curves -- 5.2.2 Comparing ROC Curves at a Particular Point -- 5.2.3 Determining the Range of FPRs for which TPRs Differ -- 5.2.4 Comparison of the Area or Partial Area -- 5.3 Tests of Equivalence -- 5.3.1 Testing Whether ROC Curve Areas are Equivalent: Case Study 3 -- 6 Sample Size Calculations -- 6.1 Studies Estimating the Accuracy of a Single Test -- 6.1.1 Sample Size Calculations for Estimating Sensitivity and/or Specificity - Case Study 1 -- 6.1.2 Sample Size for Estimating the Area Under the ROC Curve - Case Study 2.
6.1.3 Studies with Clustered Data -- 6.1.4 Testing the Hypothesis that the ROC Area is Equal to a Particular Value -- 6.1.5 Sample Size for Estimating Sensitivity at Fixed FPR - Case Study 2 -- 6.1.6 Sample Size for Estimating the Partial Area Under the ROC Curve - Case Study 2 -- 6.2 Sample Size for Detecting a Difference in Accuracies of Two Tests -- 6.2.1 Sample Size Software -- 6.2.2 Sample Size for Comparing Tests' Sensitivity and/or Specificity - Case Study 1 -- 6.2.3 Sample Size for Comparing Tests' Positive and Negative Predictive Values - Case Study 1 -- 6.2.4 Sample Size for Comparing Tests' Area Under the ROC Curve - Case Study 2 -- 6.2.5 Sample Size for Comparing Tests with Clustered Data -- 6.2.6 Sample Size for Comparing Tests' Sensitivity at Fixed FPR - Case Study 2 -- 6.2.7 Sample Size for Comparing Tests' Partial Area Under the ROC Curve - Case Study 2 -- 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 Determination for Multi-Reader Studies -- 6.5.1 MRMC Sample Size Software -- 6.5.2 MRMC Sample Size Calculations with No Pilot Data -- 6.5.3 MRMC Sample Size Calculations with Pilot Data -- 6.6 Alternative to Sample Size Formulae -- 7 Introduction to Meta-analysis for Diagnostic Accuracy Studies -- 7.1 Objectives -- 7.2 Retrieval of the Literature -- 7.2.1 Literature Search: Meta-analysis of Ultrasound for PAD -- 7.3 Inclusion/Exclusion Criteria -- 7.3.1 Inclusion/Exclusion Criteria: Meta-analysis of Ultrasound for PAD -- 7.4 Extracting Information from the Literature -- 7.4.1 Data Abstraction: Meta-analysis of Ultrasound for PAD -- 7.5 Statistical Analysis -- 7.5.1 Binary-Scale Data -- 7.5.2 Ordinal- or Continuous- Scale Data -- 7.5.3 Area Under the ROC Curve -- 7.5.4 Other Methods -- 7.6 Public Presentation.
7.6.1 Presentation of Results: Meta-analysis of Ultrasound for PAD -- PART II ADVANCED METHODS -- 8 Regression Analysis for Independent ROC Data -- 8.1 Four Clinical Studies -- 8.1.1 Surgical Lesion in a Carotid Vessel Example -- 8.1.2 Pancreatic Cancer Example -- 8.1.3 Hearing Test Example -- 8.1.4 Staging of Prostate Cancer Example -- 8.2 Regression Models for Continuous-Scale Tests -- 8.2.1 Indirect Regression Models for ROC Curves -- 8.2.2 Direct Regression Models for ROC Curves -- 8.3 Regression Models for Ordinal-Scale Tests -- 8.3.1 Indirect Regression Models for Latent Smooth ROC Curves -- 8.3.2 Direct Regression Model for Latent Smooth ROC Curves -- 8.3.3 Detection of Periprostatic Invasion with Ultrasound -- 8.4 Covariate Adjusted ROC Curves of Continuous-Scale tests -- 9 Analysis of Multiple Reader and/or Multiple Test Studies -- 9.1 Studies Comparing Multiple Tests with Covariates -- 9.1.1 Two Clinical Studies -- 9.1.2 Indirect Regression Models for Ordinal-Scale Tests -- 9.1.3 Direct Regression Models for Continuous-scale Tests -- 9.2 Studies with Multiple Readers and Multiple Tests -- 9.2.1 Three MRMC Studies -- 9.2.2 Statistical Methods for Analyzing MRMC Studies -- 9.2.3 Analysis of the Interstitial Disease Example -- 9.2.4 Comparisons between MRMC Methods -- 9.3 Analysis of Multiple Tests Designed to Locate and Diagnose Lesions -- 9.3.1 LROC Approach -- 9.3.2 FROC Approach -- 9.3.3 ROI Approach -- 10 Methods for Correcting Verification Bias -- 10.1 Examples -- 10.1.1 Hepatic Scintigraph -- 10.1.2 Screening Tests for Dementia Disorder Example -- 10.1.3 Fever of Uncertain Origin -- 10.1.4 CT and MRI for Staging Pancreatic Cancer Example -- 10.1.5 NACC MDS on Alzheimer Disease (AD) -- 10.2 Impact of Verification Bias -- 10.3 A Single Binary-Scale Test -- 10.3.1 Correction Methods Under the MAR Assumption.
10.3.2 Correction Methods Without the MAR Assumption.
Summary: 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®, 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 valuableSummary: reference for clinicians and researchers working in the fields of medicine, epidemiology, and biostatistics.
Tags from this library: No tags from this library for this title. Log in to add tags.
    Average rating: 0.0 (0 votes)
No physical items for this record

Intro -- 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.

2.10 Detection and Localization of Multiple Lesions -- 2.11 Positive and Negative Predictive Values, Bayes Theorem, and Case Study 2 -- 2.11.1 Bayes Theorem -- 2.12 Optimal Decision Threshold on the ROC Curve -- 2.12.1 Optimal Thresholds for Maximizing Classification -- 2.12.2 Optimal Threshold for Minimizing Cost -- 2.12.3 Optimal Decision Threshold: Rapid Eye Movement as a Marker for Depression Example -- 2.13 Interpreting the Results of Multiple Tests -- 2.13.1 Parallel Testing -- 2.13.2 Serial, or Sequential, Testing -- 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 -- 3.3.1 Phase I: Exploratory Studies -- 3.3.2 Phase II: Challenge Studies -- 3.3.3 Phase III: Clinical Studies -- 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 -- 3.8.1 Format for Test Results -- 3.8.2 Data Collection for Reader Studies -- 3.8.3 Reader Training -- 3.9 Plan Data Analyses -- 3.9.1 Statistical Hypotheses -- 3.9.2 Planning for Covariate Adjustment -- 3.9.3 Reporting Test Results -- 3.10 Determine Sample Size -- 4 Estimation and Hypothesis Testing in a Single Sample -- 4.1 Binary-Scale Data -- 4.1.1 Sensitivity and Specificity -- 4.1.2 Predictive Value of a Positive or Negative -- 4.1.3 Sensitivity, Specificity and Predictive Values with Clustered Binary-Scale Data -- 4.1.4 Likelihood Ratio (LR) -- 4.1.5 Odds Ratio -- 4.2 Ordinal-Scale Data -- 4.2.1 Empirical ROC Curve -- 4.2.2 Fitting a Smooth Curve -- 4.2.3 Estimation of Sensitivity at a Particular False Positive Rate -- 4.2.4 Area and Partial Area under the ROC Curve (Parametric Methods) -- 4.2.5 Confidence Interval Estimation.

4.2.6 Area and Partial Area Under the ROC Curve (Nonparametric Methods) -- 4.2.7 Nonparametric Analysis of Clustered Data. -- 4.2.8 Degenerate Data -- 4.2.9 Choosing Between Parametric, Semi-parametric and Nonparametric Methods -- 4.3 Continuous-Scale Data -- 4.3.1 Empirical ROC Curve -- 4.3.2 Fitting a Smooth ROC Curve - Parametric, Semi-parametric and Nonparametric Methods -- 4.3.3 Confidence Bands Around the Estimated ROC Curve -- 4.3.4 Area and Partial Area Under the ROC Curve - Parametric, Nonparametric and Semi-parametric Methods -- 4.3.5 Confidence Intervals for the Area Under the ROC Curve -- 4.3.6 Fixed False Positive Rate - Sensitivity and the Decision Threshold -- 4.3.7 Choosing the Optimal Operating Point and Decision Threshold -- 4.3.8 Choosing between Parametric, Semi-parametric and Nonparametric Methods -- 4.4 Testing the Hypothesis that the ROC Curve Area or Partial Area Is a Specific Value -- 4.4.1 Testing Whether MRA has Any Ability to Detect Significant Carotid Stenosis -- 5 Comparing the Accuracy of Two Diagnostic Tests -- 5.1 Binary-Scale Data -- 5.1.1 Sensitivity and Specificity -- 5.1.2 Sensitivity and Specificity of Clustered Binary Data -- 5.1.3 Predictive Probability of a Positive or Negative -- 5.2 Ordinal- and Continuous-Scale Data -- 5.2.1 Testing the Equality of Two ROC Curves -- 5.2.2 Comparing ROC Curves at a Particular Point -- 5.2.3 Determining the Range of FPRs for which TPRs Differ -- 5.2.4 Comparison of the Area or Partial Area -- 5.3 Tests of Equivalence -- 5.3.1 Testing Whether ROC Curve Areas are Equivalent: Case Study 3 -- 6 Sample Size Calculations -- 6.1 Studies Estimating the Accuracy of a Single Test -- 6.1.1 Sample Size Calculations for Estimating Sensitivity and/or Specificity - Case Study 1 -- 6.1.2 Sample Size for Estimating the Area Under the ROC Curve - Case Study 2.

6.1.3 Studies with Clustered Data -- 6.1.4 Testing the Hypothesis that the ROC Area is Equal to a Particular Value -- 6.1.5 Sample Size for Estimating Sensitivity at Fixed FPR - Case Study 2 -- 6.1.6 Sample Size for Estimating the Partial Area Under the ROC Curve - Case Study 2 -- 6.2 Sample Size for Detecting a Difference in Accuracies of Two Tests -- 6.2.1 Sample Size Software -- 6.2.2 Sample Size for Comparing Tests' Sensitivity and/or Specificity - Case Study 1 -- 6.2.3 Sample Size for Comparing Tests' Positive and Negative Predictive Values - Case Study 1 -- 6.2.4 Sample Size for Comparing Tests' Area Under the ROC Curve - Case Study 2 -- 6.2.5 Sample Size for Comparing Tests with Clustered Data -- 6.2.6 Sample Size for Comparing Tests' Sensitivity at Fixed FPR - Case Study 2 -- 6.2.7 Sample Size for Comparing Tests' Partial Area Under the ROC Curve - Case Study 2 -- 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 Determination for Multi-Reader Studies -- 6.5.1 MRMC Sample Size Software -- 6.5.2 MRMC Sample Size Calculations with No Pilot Data -- 6.5.3 MRMC Sample Size Calculations with Pilot Data -- 6.6 Alternative to Sample Size Formulae -- 7 Introduction to Meta-analysis for Diagnostic Accuracy Studies -- 7.1 Objectives -- 7.2 Retrieval of the Literature -- 7.2.1 Literature Search: Meta-analysis of Ultrasound for PAD -- 7.3 Inclusion/Exclusion Criteria -- 7.3.1 Inclusion/Exclusion Criteria: Meta-analysis of Ultrasound for PAD -- 7.4 Extracting Information from the Literature -- 7.4.1 Data Abstraction: Meta-analysis of Ultrasound for PAD -- 7.5 Statistical Analysis -- 7.5.1 Binary-Scale Data -- 7.5.2 Ordinal- or Continuous- Scale Data -- 7.5.3 Area Under the ROC Curve -- 7.5.4 Other Methods -- 7.6 Public Presentation.

7.6.1 Presentation of Results: Meta-analysis of Ultrasound for PAD -- PART II ADVANCED METHODS -- 8 Regression Analysis for Independent ROC Data -- 8.1 Four Clinical Studies -- 8.1.1 Surgical Lesion in a Carotid Vessel Example -- 8.1.2 Pancreatic Cancer Example -- 8.1.3 Hearing Test Example -- 8.1.4 Staging of Prostate Cancer Example -- 8.2 Regression Models for Continuous-Scale Tests -- 8.2.1 Indirect Regression Models for ROC Curves -- 8.2.2 Direct Regression Models for ROC Curves -- 8.3 Regression Models for Ordinal-Scale Tests -- 8.3.1 Indirect Regression Models for Latent Smooth ROC Curves -- 8.3.2 Direct Regression Model for Latent Smooth ROC Curves -- 8.3.3 Detection of Periprostatic Invasion with Ultrasound -- 8.4 Covariate Adjusted ROC Curves of Continuous-Scale tests -- 9 Analysis of Multiple Reader and/or Multiple Test Studies -- 9.1 Studies Comparing Multiple Tests with Covariates -- 9.1.1 Two Clinical Studies -- 9.1.2 Indirect Regression Models for Ordinal-Scale Tests -- 9.1.3 Direct Regression Models for Continuous-scale Tests -- 9.2 Studies with Multiple Readers and Multiple Tests -- 9.2.1 Three MRMC Studies -- 9.2.2 Statistical Methods for Analyzing MRMC Studies -- 9.2.3 Analysis of the Interstitial Disease Example -- 9.2.4 Comparisons between MRMC Methods -- 9.3 Analysis of Multiple Tests Designed to Locate and Diagnose Lesions -- 9.3.1 LROC Approach -- 9.3.2 FROC Approach -- 9.3.3 ROI Approach -- 10 Methods for Correcting Verification Bias -- 10.1 Examples -- 10.1.1 Hepatic Scintigraph -- 10.1.2 Screening Tests for Dementia Disorder Example -- 10.1.3 Fever of Uncertain Origin -- 10.1.4 CT and MRI for Staging Pancreatic Cancer Example -- 10.1.5 NACC MDS on Alzheimer Disease (AD) -- 10.2 Impact of Verification Bias -- 10.3 A Single Binary-Scale Test -- 10.3.1 Correction Methods Under the MAR Assumption.

10.3.2 Correction Methods Without the MAR Assumption.

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®, 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.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

There are no comments on this title.

to post a comment.

Powered by Koha