Measurement Error and Misclassification in Statistics and Epidemiology Impacts and Bayesian Adjustments Chapman & Hall/CRC Interdisciplinary Statistics Series
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."
Date de parution : 09-2003
Ouvrage de 188 p.
15.6x23.4 cm
Thèmes de Measurement Error and Misclassification in Statistics... :
Mots-clés :
Nondifferential Misclassification; attenuation; Attenuation Factor; factor; Exposure Prevalences; explanatory; Nonidentifiable Model; variable; Posterior Distribution; exposure; Large Sample Limiting; prevalences; Binary Explanatory Variable; conditional; Prior Distribution; expectation; MCMC Output; outcome; MCMC Algorithm; bayesian; Nondifferential Measurement Error; Conditional Expectation; Weighed Diet Record; Additive Measurement Error; Synthetic Datasets; Apparent Exposure; Log Odds Ratio; Gibbs Sampler; Model Misspecification; Exposure Assessment; Attenuation Curves; Misclassification Probabilities; Measurement Error; Full Conditional Posterior Distribution; Conditionally Independent