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More About This Textbook
Overview
Mismeasurement of explanatory variables is a common hazard when using statistical modeling techniques, and particularly so in fields such as biostatistics and epidemiology where perceived risk factors cannot always be measured accurately. With this perspective and a focus on both continuous and categorical variables, Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments examines the consequences and Bayesian remedies in those cases where the explanatory variable cannot be measured with precision.
The author explores both measurement error in continuous variables and misclassification in discrete variables, and shows how Bayesian methods might be used to allow for mismeasurement. A broad range of topics, from basic research to more complex concepts such as "wrong-model" fitting, make this a useful research work for practitioners, students and researchers in biostatistics and epidemiology."
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Table of Contents
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