Measurement Error and Misclassification in Statistics and Epidemiology

<p>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.<br><br>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.</p>