- Statistics in Medicine, 2005
This book provides a good overview of recent topics in measurement error models in the linear and logistic regression context using the Bayesian paradigm .
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