Synopses & Reviews
Synopsis
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.
Synopsis
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.