Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments (Hardcover, UK)

INTRODUCTIONExamples of Mismeasurement The Mismeasurement PhenomenonWhat is Ahead?THE IMPACT OF MISMEASURED CONTINUOUS VARIABLESThe Archetypical ScenarioMore General ImpactMultiplicative Measurement ErrorMultiple Mismeasured PredictorsWhat about Variability and Small Samples?Logistic RegressionBeyond Nondifferential and Unbiased Measurement ErrorSummaryMathematical DetailsTHE IMPACT OF MISMEASURED CATEGORICAL VARIABLESThe Linear Model CaseMore General ImpactInferences on Odds-RatiosLogistic RegressionDifferential MisclassificationPolychotomous Variables SummaryMathematical DetailsADJUSTMENT FOR MISMEASURED CONTINUOUS VARIABLESPosterior DistributionsA Simple ScenarioNonlinear Mixed Effects Model: Viral DynamicsLogistic Regression I: Smoking and Bladder CancerLogistic Regression II: Framingham Heart StudyIssues in Specifying the Exposure ModelMore Flexible Exposure ModelsRetrospective AnalysisComparison with Non-Bayesian ApproachesSummaryMathematical DetailsADJUSTMENT FOR MISMEASURED CATEGORICAL VARIABLESA Simple ScenarioPartial Knowledge of Misclassification ProbabilitiesDual Exposure AssessmentModels with Additional Explanatory VariablesSummaryMathematical DetailsFURTHER TOPICSDichotomization of Mismeasured Continuous VariablesMismeasurement Bias and Model Misspecification BiasIdentifiability in Mismeasurement ModelsFurther RemarksAPPENDIX: BAYES-MCMC INFERENCEBayes TheoremPoint and Interval EstimatesMarkov Chain Monte CarloPrior SelectionMCMC and Unobserved StructureREFERENCES