Bayesian biostatistics and diagnostic medicine /

Bayesian biostatistics and diagnostic medicine /

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Bibliographic Details
Author / Creator:Broemeling, Lyle D., 1939-
Imprint:Boca Raton : Chapman & Hall/CRC, c2007.
Description:198 p. : ill. ; 25 cm.
Language:English
Subject:
Bayesian statistical decision theory.
Diagnosis -- Mathematical models.
Medicine -- Research -- Statistical methods.
Bayes Theorem.
Diagnostic Techniques and Procedures.
Data Interpretation, Statistical.
Bayesian statistical decision theory.
Diagnosis -- Mathematical models.
Medicine -- Research -- Statistical methods.
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/7178586
Hidden Bibliographic Details
ISBN:9781584887676 (alk. paper)
1584887672 (alk. paper)
Notes:Includes bibliographical references and index.
Table of Contents:
  • Preface
  • Acknowledgments
  • Author
  • 1. Introduction
  • 1.1. Introduction
  • 1.2. Statistical Methods in Diagnostic Medicine
  • 1.3. Preview of Book
  • 1.4. Datasets for the Book
  • 1.5. Software
  • References
  • 2. Diagnostic Medicine
  • 2.1. Introduction
  • 2.2. Imaging Modalities
  • 2.3. Activities in Diagnostic Imaging
  • 2.4. Accuracy and Agreement
  • 2.5. Developmental Trials for Imaging
  • 2.6. Protocol Review and Clinical Trials
  • 2.6.1. The Protocol
  • 2.6.2. Phase I, II, and III Clinical Designs
  • 2.7. The Literature
  • References
  • 3. Other Diagnostic Procedures
  • 3.1. Introduction
  • 3.2. Sentinel Lymph Node Biopsy for Melanoma
  • 3.3. Tumor Depth to Diagnose Metastatic Melanoma
  • 3.4. Biopsy for Nonsmall Cell Lung Cancer
  • 3.5. Coronary Artery Disease
  • References
  • 4. Bayesian Statistics
  • 4.1. Introduction
  • 4.2. Bayes Theorem
  • 4.3. Prior Information
  • 4.4. Posterior Information
  • 4.5. Inference
  • 4.5.1. Introduction
  • 4.5.2. Estimation
  • 4.5.3. Testing Hypotheses
  • 4.5.3.1. Introduction
  • 4.5.3.2. Binomial Example of Testing
  • 4.5.3.3. Comparing Two Binomial Populations
  • 4.5.3.4. Sharp Null Hypothesis for the Normal Mean
  • 4.6. Sample Size
  • 4.6.1. Introduction
  • 4.6.2. A One-Sample Binomial for Response
  • 4.6.3. A One-Sample Binomial with Prior Information
  • 4.6.4. Comparing Two Binomial Populations
  • 4.7. Computing
  • 4.7.1. Introduction
  • 4.7.2. Direct Methods of Computation
  • 4.7.3. Gibbs Sampling
  • 4.7.3.1. Introduction
  • 4.7.3.2. Common Mean of Normal Populations
  • 4.7.3.3. MCMC Sampling with WinBUGS[Registered]
  • 4.8. Exercises
  • References
  • 5. Bayesian Methods for Diagnostic Accuracy
  • 5.1. Introduction
  • 5.2. Study Design
  • 5.2.1. The Protocol
  • 5.2.2. Objectives
  • 5.2.3. Background
  • 5.2.4. Patient and Reader Selection
  • 5.2.5. Study Plan
  • 5.2.6. Number of Patients
  • 5.2.7. Statistical Design and Analysis
  • 5.2.8. References
  • 5.3. Bayesian Methods for Test Accuracy: Binary and Ordinal Data
  • 5.3.1. Introduction
  • 5.3.2. Classification Probabilities
  • 5.3.3. Predictive Values
  • 5.3.4. Diagnostic Likelihood Ratios
  • 5.3.5. ROC Curve
  • 5.4. Bayesian Methods for Test Accuracy: Quantitative Variables
  • 5.4.1. Introduction
  • 5.4.2. The Spokane Heart Study
  • 5.4.3. ROC Area
  • 5.4.4. Definition of the ROC Curve
  • 5.4.5. Choice of Optimal Threshold Value
  • 5.5. Clustered Data: Detection and Localization
  • 5.5.1. Introduction
  • 5.5.2. Bayesian ROC Curve for Clustered Information
  • 5.5.3. Clustered Data in Mammography
  • 5.6. Comparing Accuracy between Modalities
  • 5.7. Sample Size Determination
  • 5.7.1. Introduction
  • 5.7.2. Discrete Diagnostic Scores
  • 5.7.2.1. Binary Tests
  • 5.7.2.2. Multinomial Outcomes
  • 5.7.3. Sample Sizes: Continuous Diagnostic Scores
  • 5.7.3.1. One ROC Curve
  • 5.7.3.2. Two ROC Curves
  • 5.8. Exercises
  • References
  • 6. Regression and Test Accuracy
  • 6.1. Introduction
  • 6.2. Audiology Study
  • 6.2.1. Introduction
  • 6.2.2. Log Link Function
  • 6.2.3. Logistic Link
  • 6.2.4. Diagnostic Likelihood Ratio
  • 6.3. ROC Area and Patient Covariates
  • 6.3.1. Introduction
  • 6.3.2. ROC Curve as Response to Therapy
  • 6.3.3. Diagnosing Prostate Cancer
  • 6.4. Exercises
  • References
  • 7. Agreement
  • 7.1. Introduction
  • 7.2. Agreement for Discrete Ratings
  • 7.2.1. Binary Scores
  • 7.2.2. Other Indices of Agreement
  • 7.2.3. A Bayesian Version of McNemar
  • 7.2.4. Comparing Two Kappa Parameters
  • 7.2.5. Kappa and Stratification
  • 7.2.6. Multiple Categories and Two Readers
  • 7.2.7. Multiple Categories
  • 7.2.8. Agreement and Covariate Information
  • 7.3. Agreement for a Continuous Response
  • 7.3.1. Introduction
  • 7.3.2. Intra-Class Correlation Coefficient
  • 7.3.2.1. One-Way Random Model
  • 7.3.2.2. Two-Way Random Model
  • 7.3.3. Regression and Agreement
  • 7.4. Combining Reader Information
  • 7.5. Exercises
  • References
  • 8. Diagnostic Imaging and Clinical Trials
  • 8.1. Introduction
  • 8.2. Clinical Trials
  • 8.2.1. Introduction
  • 8.2.2. Phase I Designs
  • 8.2.3. Phase II Trials
  • 8.2.4. Phase III Trials
  • 8.3. Protocol
  • 8.4. Guidelines for Tumor Response
  • 8.5. Bayesian Sequential Stopping Rules
  • 8.6. Software for Clinical Trials
  • 8.6.1. CRM Simulator for Phase I Trials
  • 8.6.2. Multc Lean for Phase II Trials
  • 8.7. Examples
  • 8.7.1. Phase I Trial for Renal Cell Carcinoma
  • 8.7.2. An Ideal Phase II Trial
  • 8.7.3. Phase II Trial for Advanced Melanoma
  • 8.8. Exercises
  • References
  • 9. Other Topics
  • 9.1. Introduction
  • 9.2. Imperfect Diagnostic Test Procedures
  • 9.2.1. Extreme Verification Bias
  • 9.2.2. Verification Bias
  • 9.2.3. Estimating Test Accuracy with No Gold Standard
  • 9.3. Test Accuracy and Survival Analysis
  • 9.4. ROC Curves with a Non-Binary Gold Standard
  • 9.5. Periodic Screening in Cancer
  • 9.5.1. Inference for Sensitivity and Transition Probability
  • 9.5.2. Bayesian Inference for Lead-Time
  • 9.6. Decision Theory and Diagnostic Accuracy
  • 9.7. Exercises
  • References
  • Index
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