| Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments UK Edition Contributor(s): Gustafson, Paul (Author) |
|
 |
ISBN: 1584883359 ISBN-13: 9781584883357 Publisher: CRC Press OUR PRICE: $190.00 Product Type: Hardcover - Other Formats Published: September 2003 Annotation: 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." |
| Additional Information |
| BISAC Categories: - Medical | Biostatistics - Mathematics | Probability & Statistics - Bayesian Analysis - Medical | Epidemiology |
| Dewey: 511.43 |
| LCCN: 2003047290 |
| Series: Chapman & Hall/CRC Interdisciplinary Statistics |
| Physical Information: 0.63" H x 6.36" W x 9.5" (0.93 lbs) 200 pages |
| Descriptions, Reviews, Etc. |
| Publisher 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. |