Measurement Error and Misclassification in Statistics and Epidemiology

Measurement Error and Misclassification in Statistics and Epidemiology
Author: Paul Gustafson
Publisher: CRC Press
Total Pages: 213
Release: 2003-09-25
Genre: Mathematics
ISBN: 0203502760

Download Measurement Error and Misclassification in Statistics and Epidemiology Book in PDF, Epub and Kindle

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 Misclassi

Statistical Analysis with Measurement Error or Misclassification

Statistical Analysis with Measurement Error or Misclassification
Author: Grace Y. Yi
Publisher: Springer
Total Pages: 497
Release: 2017-08-02
Genre: Mathematics
ISBN: 1493966405

Download Statistical Analysis with Measurement Error or Misclassification Book in PDF, Epub and Kindle

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 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 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.

Measurement Error and Misclassification in Statistics and Epidemiology

Measurement Error and Misclassification in Statistics and Epidemiology
Author: Paul Gustafson
Publisher: CRC Press
Total Pages: 200
Release: 2003-09-25
Genre: Mathematics
ISBN: 1135441235

Download Measurement Error and Misclassification in Statistics and Epidemiology Book in PDF, Epub and Kindle

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 Misclassi

Handbook of Measurement Error Models

Handbook of Measurement Error Models
Author: Grace Y. Yi
Publisher: CRC Press
Total Pages: 648
Release: 2021-09-28
Genre: Mathematics
ISBN: 1351588591

Download Handbook of Measurement Error Models Book in PDF, Epub and Kindle

Measurement error arises ubiquitously in applications and has been of long-standing concern in a variety of fields, including medical research, epidemiological studies, economics, environmental studies, and survey research. While several research monographs are available to summarize methods and strategies of handling different measurement error problems, research in this area continues to attract extensive attention. The Handbook of Measurement Error Models provides overviews of various topics on measurement error problems. It collects carefully edited chapters concerning issues of measurement error and evolving statistical methods, with a good balance of methodology and applications. It is prepared for readers who wish to start research and gain insights into challenges, methods, and applications related to error-prone data. It also serves as a reference text on statistical methods and applications pertinent to measurement error models, for researchers and data analysts alike. Features: Provides an account of past development and modern advancement concerning measurement error problems Highlights the challenges induced by error-contaminated data Introduces off-the-shelf methods for mitigating deleterious impacts of measurement error Describes state-of-the-art strategies for conducting in-depth research

Correcting for Measurement Error and Misclassification Using General Location Models

Correcting for Measurement Error and Misclassification Using General Location Models
Author: Muhire Honorine Kwizera
Publisher:
Total Pages: 0
Release: 2023
Genre:
ISBN:

Download Correcting for Measurement Error and Misclassification Using General Location Models Book in PDF, Epub and Kindle

The proposed method uses observed data from both the calibration and main study samples and incorporates relationships among all variables in measurement error adjustment, unlike existing methods that only use the calibration data for model estimation. We assume by strong nondifferential measurement error (sNDME) that the measurement error is independent of all the error-free variables given the true value of the error-prone variable. The sNDME assumption allows us to identify our model parameters. We show through simulations that the proposed method yields reduced bias, smaller mean squared error, and interval coverage closer to the nominal level compared to existing methods in regression settings. Furthermore, this improvement is pronounced with increased measurement error, higher correlation between covariates, and stronger covariate effects. We apply the new method to the New York City Neighborhood Asthma and Allergy Study to examine the association between indoor allergen concentrations and asthma morbidity among urban asthmatic children.

Measurement Error

Measurement Error
Author: John P. Buonaccorsi
Publisher: CRC Press
Total Pages: 465
Release: 2010-03-02
Genre: Mathematics
ISBN: 1420066587

Download Measurement Error Book in PDF, Epub and Kindle

Over the last 20 years, comprehensive strategies for treating measurement error in complex models and accounting for the use of extra data to estimate measurement error parameters have emerged. Focusing on both established and novel approaches, Measurement Error: Models, Methods, and Applications provides an overview of the main techniques and illu

Applying Quantitative Bias Analysis to Epidemiologic Data

Applying Quantitative Bias Analysis to Epidemiologic Data
Author: Timothy L. Lash
Publisher: Springer Science & Business Media
Total Pages: 200
Release: 2011-04-14
Genre: Medical
ISBN: 0387879595

Download Applying Quantitative Bias Analysis to Epidemiologic Data Book in PDF, Epub and Kindle

Bias analysis quantifies the influence of systematic error on an epidemiology study’s estimate of association. The fundamental methods of bias analysis in epi- miology have been well described for decades, yet are seldom applied in published presentations of epidemiologic research. More recent advances in bias analysis, such as probabilistic bias analysis, appear even more rarely. We suspect that there are both supply-side and demand-side explanations for the scarcity of bias analysis. On the demand side, journal reviewers and editors seldom request that authors address systematic error aside from listing them as limitations of their particular study. This listing is often accompanied by explanations for why the limitations should not pose much concern. On the supply side, methods for bias analysis receive little attention in most epidemiology curriculums, are often scattered throughout textbooks or absent from them altogether, and cannot be implemented easily using standard statistical computing software. Our objective in this text is to reduce these supply-side barriers, with the hope that demand for quantitative bias analysis will follow.

Measurement Error and Misclassification

Measurement Error and Misclassification
Author: Arie Kapteyn
Publisher:
Total Pages: 0
Release: 2008
Genre:
ISBN:

Download Measurement Error and Misclassification Book in PDF, Epub and Kindle

The authors provide both a theoretical and empirical analysis of the relation between register and survey data. By distinguishing between different sources of deviations between survey and register data the authors are able to reproduce several stylized facts in the literature. In doing so, they deviate from the almost universal assumption that the register data represent the truth. They illustrate the implications of different error sources for estimation in (simple) econometric models. The analysis is applied to Swedish data that have been collected for a validation study as part of a larger European health and retirement study (SHARE: Survey of Health, Ageing, and Retirement in Europe). Thus this paper makes two contributions: (1) it adds to the limited number of empirical validation studies of earnings measurement in surveys and (2) it shows the sensitivity of some findings in the literature for the assumption that register data represent the truth. They find in particular that the common finding of substantial mean reversion in survey data largely goes away once we allow for a richer error structure.