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

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."

About the author

Paul Gustafson (University of British Columbia, Vancouver, Canada) (Author)

Summary

Addresses statistical challenges posed by inaccurately measuring explanatory variables, a common problem in biostatistics and epidemiology. This book explores both measurement error in continuous variables and misclassification in categorical variables. It is suitable for biostatisticians, epidemiologists, and students.

Product details

• Authors: Paul Gustafson, Gustafson Paul
• Assisted by: Byron J.T. Morgan (Editor of the series), Niels Keiding (Editor of the series), Peter Van der Heijden (Editor of the series), Terry Speed (Editor of the series)
• Publisher: Taylor and Francis
• Content: Book
• Product form: Hardback
• Publication date: 25.09.2003
• Subject: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematica
• EAN: 9781584883357
• ISBN: 978-1-58488-335-7
• Pages: 200
• Illustrations: schwarz-weiss Illustrationen, Tabellen, schwarz-weiss
• Dimensions (packing): 15.6 x 23.4 x 1.6 cm
• Weight (packing): 417 g
• Series: Chapman & Hall/CRC Interdisciplinary Statistics
• Subjects: Epidemiology & medical statistics, MEDICAL / Biostatistics, MEDICAL / Epidemiology, OUTCOME, Exposure, Epidemiology and Medical statistics, conditional, Monte Carlo Simulation, Conditional expectation, Bayesian, Expectation, measurement error, exposure assessment, Posterior Distribution, prior distribution, Bayesian inference methods, Gibbs sampler, latent variable analysis, Log Odds Ratio, MCMC Algorithm, biostatistical modeling, model misspecification, epidemiologic data analysis, Bayesian correction for measurement error, exposure misclassification, synthetic datasets, Attenuation Factor, Misclassification Probabilities, prevalences, Nondifferential Misclassification, Attenuation Curves, Exposure Prevalences, Full Conditional Posterior Distribution, MCMC Output, Apparent Exposure, Large Sample Limiting, Binary Explanatory Variable, Conditionally Independent, Weighed Diet Record, Nonidentifiable Model, Additive Measurement Error, Nondifferential Measurement Error