Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments

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Presents a new, modern approach to measurement error in continuous explanatory variables and misclassification in categorical explanatory variables Discusses the Bayesian impacts on measurement error-the first book to do so Contains a balance of basic pedagogic and research-oriented material Unifies the epidemiology and statistics literature on the subject, making it accessible to researchers in both field Presents technical and mathematical details at the end of each chapter to make the book easy to use This book addresses the challenge of accurately measuring explanatory variables, a common problem in biostatistics and epidemiology. The author explores both measurement error in continuous variables and misclassification in categorical variables. He also describes the circumstances in which it is necessary to explicitly adjust for imprecise covariates using the Bayesian approach and a Markov chain Monte Carlo algorithm. The book offers a mix of basic and more specialized topics such as "wrong-model" fitting. Mathematical details are featured in the final sections of each chapter. Because of its dual approach, the book is a useful reference for biostatisticians, epidemiologists, and students.

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Product Details

Table of Contents

Examples of Mismeasurement
The Mismeasurement Phenomenon
What is Ahead?
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
Mathematical Details
The Linear Model Case
More General Impact
Inferences on Odds-Ratios
Logistic Regression
Differential Misclassification
Polychotomous Variables
Mathematical Details
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
Mathematical Details
A Simple Scenario
Partial Knowledge of Misclassification Probabilities
Dual Exposure Assessment
Models with Additional Explanatory Variables
Mathematical Details
Dichotomization of Mismeasured Continuous Variables
Mismeasurement Bias and Model Misspecification Bias
Identifiability in Mismeasurement Models
Further Remarks
Bayes Theorem
Point and Interval Estimates
Markov Chain Monte Carlo
Prior Selection
MCMC and Unobserved Structure

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