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 cat
Statistical analysis with measurement error or misclassification strategy, method and application
β Scribed by Yi, Grace Y
- Publisher
- Springer, published by Springer Nature
- Year
- 2017
- Tongue
- English
- Leaves
- 497
- Series
- Springer series in statistics
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
This monograph on measurement error and misclassification covers a broad range of problems and 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: Strategy, Method and Application brings together assorted methods in a single text and provides an update of recent developments for a variety of settings. Measurement 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 methodsβsuch as likelihood and estimating function theoryβor modeling schemes in varying settingsβsuch as survival analysis and longitudinal data analysisβcan result in a full appreciation of the material, but it is not essential since each chapter provides basic inference frameworks and background information on an individual topic to ease the access of the material. The text is presented in a coherent and self-contained manner and highlights the essence of commonly used modeling and inference methods.
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 statistics and biostatistics; or as a book for applied statisticians whose interest focuses on analysis of error-contaminated data.
Grace Y. Yi is Professor of Statistics and University Research Chair at the University of Waterloo. She is the 2010 winner of the CRM-SSC Prize, an honor awarded in recognition of a statistical scientist's professional accomplishments in research during the first 15 years after having received a doctorate. She is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute.
β¦ Table of Contents
Front Matter ....Pages i-xxvii
Inference Framework and Method (Grace Y. Yi)....Pages 1-41
Measurement Error and Misclassification: Introduction (Grace Y. Yi)....Pages 43-85
Survival Data with Measurement Error (Grace Y. Yi)....Pages 87-150
Recurrent Event Data with Measurement Error (Grace Y. Yi)....Pages 151-191
Longitudinal Data with Covariate Measurement Error (Grace Y. Yi)....Pages 193-256
Multi-State Models with Error-Prone Data (Grace Y. Yi)....Pages 257-300
CaseβControl Studies with Measurement Error or Misclassification (Grace Y. Yi)....Pages 301-351
Analysis with Mismeasured Responses (Grace Y. Yi)....Pages 353-393
Miscellaneous Topics (Grace Y. Yi)....Pages 395-410
Back Matter ....Pages 411-479
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