Measurement error and misclassification in statistics and epidemiology : impacts and Bayesian adjustments
Author(s)
Bibliographic Information
Measurement error and misclassification in statistics and epidemiology : impacts and Bayesian adjustments
（Interdisciplinary statistics）
Chapman & Hall/CRC, c2004
Available at 10 libraries
Note
Includes bibliographical references and index
Description and Table of Contents
Description
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."
Table of Contents
Introduction. Impact of Mismeasured Continuous Variables. Impact of Mismeasured Categorical Variables. Adjustment for Mismeasured Continuous Variables. Adjustment for Mismeasured Categorical Variables. Further Topics. Appendix: Bayes-MCMC Inference. References.
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