Author: Paul Gustafson
Publisher: CRC Press
ISBN: 0203502760
Category : Mathematics
Languages : en
Pages : 213
Book Description
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
Measurement Error and Misclassification in Statistics and Epidemiology
Author: Paul Gustafson
Publisher: CRC Press
ISBN: 0203502760
Category : Mathematics
Languages : en
Pages : 213
Book Description
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
Publisher: CRC Press
ISBN: 0203502760
Category : Mathematics
Languages : en
Pages : 213
Book Description
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
Measurement Error and Misclassification in Statistics and Epidemiology
Author: Paul Gustafson
Publisher: CRC Press
ISBN: 1135441235
Category : Mathematics
Languages : en
Pages : 213
Book Description
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."
Publisher: CRC Press
ISBN: 1135441235
Category : Mathematics
Languages : en
Pages : 213
Book Description
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."
Statistical Analysis with Measurement Error or Misclassification
Author: Grace Y. Yi
Publisher: Springer
ISBN: 1493966405
Category : Mathematics
Languages : en
Pages : 497
Book Description
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.
Publisher: Springer
ISBN: 1493966405
Category : Mathematics
Languages : en
Pages : 497
Book Description
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.
Applying Quantitative Bias Analysis to Epidemiologic Data
Author: Timothy L. Lash
Publisher: Springer Science & Business Media
ISBN: 0387879595
Category : Medical
Languages : en
Pages : 200
Book Description
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.
Publisher: Springer Science & Business Media
ISBN: 0387879595
Category : Medical
Languages : en
Pages : 200
Book Description
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.
Handbook of Measurement Error Models
Author: Grace Y. Yi
Publisher: CRC Press
ISBN: 1351588591
Category : Mathematics
Languages : en
Pages : 648
Book Description
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
Publisher: CRC Press
ISBN: 1351588591
Category : Mathematics
Languages : en
Pages : 648
Book Description
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
Measurement Error
Author: John P. Buonaccorsi
Publisher: CRC Press
ISBN: 1420066587
Category : Mathematics
Languages : en
Pages : 465
Book Description
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
Publisher: CRC Press
ISBN: 1420066587
Category : Mathematics
Languages : en
Pages : 465
Book Description
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
Statistical Models in Epidemiology
Author: David Clayton
Publisher: Oxford University Press, USA
ISBN: 0199671184
Category : Mathematics
Languages : en
Pages : 376
Book Description
This self-contained account of the statistical basis of epidemiology has been written for those with a basic training in biology. It is specifically intended for students enrolled for a masters degree in epidemiology, clinical epidemiology, or biostatistics.
Publisher: Oxford University Press, USA
ISBN: 0199671184
Category : Mathematics
Languages : en
Pages : 376
Book Description
This self-contained account of the statistical basis of epidemiology has been written for those with a basic training in biology. It is specifically intended for students enrolled for a masters degree in epidemiology, clinical epidemiology, or biostatistics.
Foundations of Epidemiology
Author: Marit L. Bovbjerg
Publisher:
ISBN: 9781955101035
Category : Health & Fitness
Languages : en
Pages : 176
Book Description
Foundations of Epidemiology is an open access, introductory epidemiology text intended for students and practitioners in public or allied health fields. It covers epidemiologic thinking, causality, incidence and prevalence, public health surveillance, epidemiologic study designs and why we care about which one is used, measures of association, random error and bias, confounding and effect modification, and screening. Concepts are illustrated with numerous examples drawn from contemporary and historical public health issues.
Publisher:
ISBN: 9781955101035
Category : Health & Fitness
Languages : en
Pages : 176
Book Description
Foundations of Epidemiology is an open access, introductory epidemiology text intended for students and practitioners in public or allied health fields. It covers epidemiologic thinking, causality, incidence and prevalence, public health surveillance, epidemiologic study designs and why we care about which one is used, measures of association, random error and bias, confounding and effect modification, and screening. Concepts are illustrated with numerous examples drawn from contemporary and historical public health issues.
Concepts of Epidemiology
Author: Raj S. Bhopal
Publisher: Oxford University Press
ISBN: 0198739680
Category : Medical
Languages : en
Pages : 481
Book Description
First edition published in 2002. Second edition published in 2008.
Publisher: Oxford University Press
ISBN: 0198739680
Category : Medical
Languages : en
Pages : 481
Book Description
First edition published in 2002. Second edition published in 2008.
Nonlinear Models for Repeated Measurement Data
Author: Marie Davidian
Publisher: Routledge
ISBN: 1351428152
Category : Mathematics
Languages : en
Pages : 377
Book Description
Nonlinear measurement data arise in a wide variety of biological and biomedical applications, such as longitudinal clinical trials, studies of drug kinetics and growth, and the analysis of assay and laboratory data. Nonlinear Models for Repeated Measurement Data provides the first unified development of methods and models for data of this type, with a detailed treatment of inference for the nonlinear mixed effects and its extensions. A particular strength of the book is the inclusion of several detailed case studies from the areas of population pharmacokinetics and pharmacodynamics, immunoassay and bioassay development and the analysis of growth curves.
Publisher: Routledge
ISBN: 1351428152
Category : Mathematics
Languages : en
Pages : 377
Book Description
Nonlinear measurement data arise in a wide variety of biological and biomedical applications, such as longitudinal clinical trials, studies of drug kinetics and growth, and the analysis of assay and laboratory data. Nonlinear Models for Repeated Measurement Data provides the first unified development of methods and models for data of this type, with a detailed treatment of inference for the nonlinear mixed effects and its extensions. A particular strength of the book is the inclusion of several detailed case studies from the areas of population pharmacokinetics and pharmacodynamics, immunoassay and bioassay development and the analysis of growth curves.