Measurement Error and Misclassification in Statistics and Epidemiology

Measurement Error and Misclassification in Statistics and Epidemiology : Impacts and Bayesian Adjustments

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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."show more

Product details

  • Hardback | 200 pages
  • 160.02 x 231.14 x 17.78mm | 408.23g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • UK ed.
  • 39 black & white illustrations, 20 black & white tables
  • 1584883359
  • 9781584883357

Review quote

"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)show more

Table of contents

INTRODUCTION Examples of Mismeasurement The Mismeasurement Phenomenon What is Ahead? THE IMPACT OF MISMEASURED CONTINUOUS VARIABLES The Archetypical Scenario More General Impact Multiplicative Measurement Error Multiple Mismeasured Predictors What about Variability and Small Samples? Logistic Regression Beyond Nondifferential and Unbiased Measurement Error Summary Mathematical Details THE IMPACT OF MISMEASURED CATEGORICAL VARIABLES The Linear Model Case More General Impact Inferences on Odds-Ratios Logistic Regression Differential Misclassification Polychotomous Variables Summary Mathematical Details ADJUSTMENT FOR MISMEASURED CONTINUOUS VARIABLES Posterior Distributions A Simple Scenario Nonlinear Mixed Effects Model: Viral Dynamics Logistic Regression I: Smoking and Bladder Cancer Logistic Regression II: Framingham Heart Study Issues in Specifying the Exposure Model More Flexible Exposure Models Retrospective Analysis Comparison with Non-Bayesian Approaches Summary Mathematical Details ADJUSTMENT FOR MISMEASURED CATEGORICAL VARIABLES A Simple Scenario Partial Knowledge of Misclassification Probabilities Dual Exposure Assessment Models with Additional Explanatory Variables Summary Mathematical Details FURTHER TOPICS Dichotomization of Mismeasured Continuous Variables Mismeasurement Bias and Model Misspecification Bias Identifiability in Mismeasurement Models Further Remarks APPENDIX: BAYES-MCMC INFERENCE Bayes Theorem Point and Interval Estimates Markov Chain Monte Carlo Prior Selection MCMC and Unobserved Structure REFERENCESshow more

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