Measurement error and misclassificaion in statistics and epidemiology : impacts and Bayesian adjustments /

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Bibliographic Details
Author / Creator:Gustafson, Paul, 1968-
Imprint:Boca Raton : Chapman & Hall/CRC, c2004.
Description:x, 188 p. : ill. ; 25 cm.
Language:English
Subject:
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/5054063
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ISBN:1584883359 (alk. paper)
Notes:Includes bibliographical references (p. 179-186) and index.
Table of Contents:
  • Preface
  • Guide to Notation
  • 1. Introduction
  • 1.1. Examples of Mismeasurement
  • 1.2. The Mismeasurement Phenomenon
  • 1.3. What is Ahead?
  • 2. The Impact of Mismeasured Continuous Variables
  • 2.1. The Archetypical Scenario
  • 2.2. More General Impact
  • 2.3. Multiplicative Measurement Error
  • 2.4. Multiple Mismeasured Predictors
  • 2.5. What about Variability and Small Samples?
  • 2.6. Logistic Regression
  • 2.7. Beyond Nondifferential and Unbiased Measurement Error
  • 2.8. Summary
  • 2.9. Mathematical Details
  • 3. The Impact of Mismeasured Categorical Variables
  • 3.1. The Linear Model Case
  • 3.2. More General Impact
  • 3.3. Inferences on Odds-Ratios
  • 3.4. Logistic Regression
  • 3.5. Differential Misclassification
  • 3.6. Polychotomous Variables
  • 3.7. Summary
  • 3.8. Mathematical Details
  • 4. Adjusting for Mismeasured Continuous Variables
  • 4.1. Posterior Distributions
  • 4.2. A Simple Scenario
  • 4.3. Nonlinear Mixed Effects Model: Viral Dynamics
  • 4.4. Logistic Regression I: Smoking and Bladder Cancer
  • 4.5. Logistic Regression II: Framingham Heart Study
  • 4.6. Issues in Specifying the Exposure Model
  • 4.7. More Flexible Exposure Models
  • 4.8. Retrospective Analysis
  • 4.9. Comparison with Non-Bayesian Approaches
  • 4.10. Summary
  • 4.11. Mathematical Details
  • 5. Adjusting for Mismeasured Categorical Variables
  • 5.1. A Simple Scenario
  • 5.2. Partial Knowledge of Misclassification Probabilities
  • 5.3. Dual Exposure Assessment
  • 5.4. Models with Additional Explanatory Variables
  • 5.5. Summary
  • 5.6. Mathematical Details
  • 6. Further Topics
  • 6.1. Dichotomization of Mismeasured Continuous Variables
  • 6.2. Mismeasurement Bias and Model Misspecification Bias
  • 6.3. Identifiability in Mismeasurement Models
  • 6.4. Further Remarks
  • Appendix. Bayes-MCMC Inference
  • A.1. Bayes Theorem
  • A.2. Point and Interval Estimates
  • A.3. Markov Chain Monte Carlo
  • A.4. Prior Selection
  • A.5. MCMC and Unobserved Structure
  • References
  • Index