Measurement Error and Misclassification in

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."

Paul Gustafson is an associate professor at the University of British Columbia, Vancouver, Canada.

Preface	p. ix
Guide to Notation	p. xi
Introduction	p. 1
Examples of Mismeasurement	p. 1
The Mismeasurement Phenomenon	p. 3
What is Ahead?	p. 6
The Impact of Mismeasured Continuous Variables	p. 9
The Archetypical Scenario	p. 9
More General Impact	p. 11
Multiplicative Measurement Error	p. 15
Multiple Mismeasured Predictors	p. 18
What about Variability and Small Samples?	p. 21
Logistic Regression	p. 24
Beyond Nondifferential and Unbiased Measurement Error	p. 26
Summary	p. 29
Mathematical Details	p. 30
The Impact of Mismeasured Categorical Variables	p. 35
The Linear Model Case	p. 35
More General Impact	p. 37
Inferences on Odds-Ratios	p. 39
Logistic Regression	p. 44
Differential Misclassification	p. 46
Polychotomous Variables	p. 47
Summary	p. 49
Mathematical Details	p. 49
Adjusting for Mismeasured Continuous Variables	p. 51
Posterior Distributions	p. 51
A Simple Scenario	p. 56
Nonlinear Mixed Effects Model: Viral Dynamics	p. 62
Logistic Regression I: Smoking and Bladder Cancer	p. 69
Logistic Regression II: Framingham Heart Study	p. 72
Issues in Specifying the Exposure Model	p. 74
More Flexible Exposure Models	p. 79
Retrospective Analysis	p. 83
Comparison with Non-Bayesian Approaches	p. 88
Summary	p. 92
Mathematical Details	p. 92
Adjusting for Mismeasured Categorical Variables	p. 99
A Simple Scenario	p. 99
Partial Knowledge of Misclassification Probabilities	p. 104
Dual Exposure Assessment	p. 111
Models with Additional Explanatory Variables	p. 119
Summary	p. 131
Mathematical Details	p. 131
Further Topics	p. 139
Dichotomization of Mismeasured Continuous Variables	p. 139
Mismeasurement Bias and Model Misspecification Bias	p. 147
Identifiability in Mismeasurement Models	p. 152
Further Remarks	p. 164
Bayes-MCMC Inference	p. 165
Bayes Theorem	p. 165
Point and Interval Estimates	p. 168
Markov Chain Monte Carlo	p. 169
Prior Selection	p. 176
MCMC and Unobserved Structure	p. 177
References	p. 179
Index	p. 187
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Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments

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