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… .
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.