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Measurement Error and Misclassification in Statistics and Epidemiology

Impacts and Bayesian Adjustments

Measurement Error and Misclassification in Statistics and Epidemiology by Paul Gustafson
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INTRODUCTION
Examples of Mismeasurement
The Mismeasurement Phenomenon
What is Ahead?
THE IMPACT OF MISMEASURED CONTINUOUS VARIABLES
The Archetypical Scenario
More General Impact
Multiplicative Measurement Error
Multiple Mismeasured Predictors
What about Variability and Small Samples?
Logistic Regression
Beyond Nondifferential and Unbiased Measurement Error
Summary
Mathematical Details
THE IMPACT OF MISMEASURED CATEGORICAL VARIABLES
The Linear Model Case
More General Impact
Inferences on Odds-Ratios
Logistic Regression
Differential Misclassification
Polychotomous Variables
Summary
Mathematical Details
ADJUSTMENT FOR MISMEASURED CONTINUOUS VARIABLES
Posterior Distributions
A Simple Scenario
Nonlinear Mixed Effects Model: Viral Dynamics
Logistic Regression I: Smoking and Bladder Cancer
Logistic Regression II: Framingham Heart Study
Issues in Specifying the Exposure Model
More Flexible Exposure Models
Retrospective Analysis
Comparison with Non-Bayesian Approaches
Summary
Mathematical Details
ADJUSTMENT FOR MISMEASURED CATEGORICAL VARIABLES
A Simple Scenario
Partial Knowledge of Misclassification Probabilities
Dual Exposure Assessment
Models with Additional Explanatory Variables
Summary
Mathematical Details
FURTHER TOPICS
Dichotomization of Mismeasured Continuous Variables
Mismeasurement Bias and Model Misspecification Bias
Identifiability in Mismeasurement Models
Further Remarks
APPENDIX: BAYES-MCMC INFERENCE
Bayes Theorem
Point and Interval Estimates
Markov Chain Monte Carlo
Prior Selection
MCMC and Unobserved Structure
REFERENCES
CRC Press; September 2003
213 pages; ISBN 9780203502761
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Title: Measurement Error and Misclassification in Statistics and Epidemiology
Author: Paul Gustafson
 
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