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

Paul Gustafson

September 25, 2003 by Chapman and Hall/CRC
Reference - 200 Pages - 39 B/W Illustrations
ISBN 9781584883357 - CAT# C3359
Series: Chapman & Hall/CRC Interdisciplinary Statistics

was $139.95

USD$111.96

SAVE ~$27.99

Add to Wish List
SAVE 25%
When you buy 2 or more print books!
See final price in shopping cart.
FREE Standard Shipping!

Features

  • Presents a new, modern approach to measurement error in continuous explanatory variables and misclassification in categorical explanatory variables
  • Discusses the Bayesian impacts for measurement error-the first book to do so
  • Contains a balance of basic pedagogic and research-oriented material
  • Unifies the epidemiology and statistics literature on the subject, making it accessible to researchers in both fields
  • Features mathematical details in the final sections of each chapter
  • Includes a primer on MCMC Analysis as an appendix
  • Offers electronic data downloadable from the author's Web site at www.stat.ubc.ca/people/gustaf
  • Summary

    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."