Statistical Analysis with Measurement Error or Misclassification

This monograph on measurement error and misclassification emphasizes unique features in modeling and analyzing problems arising from medical research and epidemiological studies. Many measurement error and misclassification problems have been addressed in various fields over the years as well as with a wide spectrum of data, including event history data (such as survival data and recurrent event data), correlated data (such as longitudinal data and clustered data), multi-state event data, and data arising from case-control studies. Statistical Analysis with Measurement Error or Misclassification brings together assorted methods in a single text and provides an update of recent developments for a variety of settings. Error effects and strategies of handling mismeasurement for different models are closely examined in combination with applications to specific problems.

Readers with diverse backgrounds and objectives can utilize this text. Familiarity with inference methods-such as likelihood and estimating function theory-or modeling schemes in varying settings-such as survival analysis and longitudinal data analysis-can result in a full appreciation of the material, but it is not essential since many chapters provide basic inference frameworks and background information.

This text can serve as a reference book for researchers interested in statistical methodology for handling data with measurement error or misclassification; as a textbook for graduate students, especially for those majoring in biostatistics; or as a book for applied statisticians whose interest focuses on analysis of error-contaminated data.

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