by Paul Gustafson
Product Details
* Hardcover: 200 pages
* Publisher: Chapman & Hall/CRC (September 25, 2003)
* Language: English
* ISBN: 1584883359
* Product Dimensions: 9.5 x 6.4 x 0.6 inches
* Amazon.com Sales Rank: #736,167 in Books
* List Price: $89.95
Review
This book shows that error-prone measurements may create serious biases and offers Bayesian approaches to attempt unbiased estimation, or ‘adjustments’. This is a useful book if you have data containing errors or if you have an interest in statistical theory of errors of measurement. As nearly all data is in some way erroneous, it is a useful book for all statisticians and mathematically inclined epidemiologists.
- Statistics in Medicine
This book provides a good overview of recent topics in measurement error models in the linear and logistic regression context using the Bayesian paradigm .
- Technometrics
a welcome addition for anyone who is interested in the topic of mismeasurement and in particular the issue of Bayesian adjustment methods. Although it does not shy away from the theoretical issues surrounding this subject, it remains accessible for practical applied statisticians. The book has two real highlights for me: firstly, the author’s focus on the problems that mismeasurement creates in a variety of complex situations, reflecting what practical statisticians deal with regularly. Secondly, the book gives almost equal treatment to the problem of mismeasurement of continuous and discrete variable; it is quite rare to see such extensive treatment of both situations in one place The examples that are used throughout the book offer great insight, as they highlight the complexities of real life data analysis when mismeasurement is an issue
Journal of the Royal Statistical Society, Series A., vol. 157(3)
This is a well-written book and contains a great deal of information on the impact of measurement error in explanatory variables, as well as details of methods to adjust for mismeasurement. Considering measurement error in both continous and categorical variables, as well as using Bayesian methods to adjust for mismeasurement, make this an excellent resource for epidemiologists or medical statisticians.
-International Journal of Epidemiology, Zoe Fewell
Book Description
This book addresses statistical challenges posed by inaccurately measuring explanatory variables, a common problem in biostatistics and epidemiology. The author explores both measurement error in continuous variables and misclassification in categorical variables. He also describes the circumstances in which it is necessary to explicitly adjust for imprecise covariates using the Bayesian approach and a Markov chain Monte Carlo algorithm. The book offers a mix of basic and more specialized topics and provides mathematical details in the final sections of each chapter. Because of its dual approach, the book is a useful reference for biostatisticians, epidemiologists, and students.
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