Applying Quantitative Bias Analysis to Epidemiologic Data

by Matthew P. Fox, Richard F. MacLehose, Timothy L. Lash

Overview

This textbook and guide focuses on methodologies for bias analysis in epidemiology and public health, not only providing updates to the first edition but also further developing methods and adding new advanced methods.

As computational power available to analysts has improved and epidemiologic problems have become more advanced, missing data, Bayes, and empirical methods have become more commonly used. This new edition features updated examples throughout and adds coverage addressing:

• Measurement error pertaining to continuous and polytomous variables
• Methods surrounding person-time (rate) data
• Bias analysis using missing data, empirical (likelihood), and Bayes methods

A unique feature of this revision is its section on best practices for implementing, presenting, and interpreting bias analyses. Pedagogically, the text guides students and professionals through the planning stages of bias analysis, including the design of validation studies and the collection of validity data from other sources. Three chapters present methods for corrections to address selection bias, uncontrolled confounding, and measurement errors, and subsequent sections extend these methods to probabilistic bias analysis, missing data methods, likelihood-based approaches, Bayesian methods, and best practices.

Product Details

ISBN-13:	9783030826734
Publisher:	Springer-Verlag New York, LLC
Publication date:	03/24/2022
Series:	Statistics for Biology and Health
Sold by:	Barnes & Noble
Format:	eBook
File size:	21 MB
Note:	This product may take a few minutes to download.

About the Author

Timothy Lash, D.Sc., M.P.H., is professor in the Department of Epidemiology at the Rollins School of Public Health and honorary professor of cancer epidemiology in the Department of Clinical Epidemiology at Aarhus University in Aarhus, Denmark. Dr. Lash is also past-President of the Society for Epidemiologic Research (SER) for the 2014-2015 term. His research focuses on predictors of cancer recurrence, including molecular predictors of treatment effectiveness and late recurrence, and he also researches methods and applications of quantitative bias analysis. 

Matthew Fox, D.Sc., M.P.H, is associate professor in the Center for Global Health&Development and in the Department of Epidemiology at Boston University. Before joining Boston University, he was a Peace Corps volunteer in the former Soviet Republic of Turkmenistan. Dr. Fox is currently funded through a K award from the National Institutes of Allergy and Infectious Diseases to work on ways to improve retention in HIV-care programs in South Africa from time of testing HIV-positive through long-term treatment. His research interests include treatment outcomes in HIV-treatment programs, infectious disease epidemiology, and epidemiological methods, including quantitative bias analysis.

Richard MacLehose, Ph.D., is associate professor in the Division of Epidemiology and Community Health at the University of Minnesota. Dr. MacLehose received his M.S. in epidemiology from the University of Washington and his Ph.D. in epidemiology from the University of North Carolina. His research interests include Bayesian statistics (including bias analysis), epidemiologic methods, applied biostatistics, and reproductive and environmental health.

Table of Contents

1. Introduction and Objectives.- 2. A Guide to Implementing Quantitative Bias Analysis.- 3. Data Sources for Bias Analysis.- 4. Selection Bias.- 5. Uncontrolled Confounders.- 6. Misclassification.- 7. Measurement Error for Continuous Variables.- 8. Multiple Bias Modeling.- 8. Bias Analysis by Simulation for Summary Level Data.- 9. Bias Analysis by Simulation for Record Level Data.- 10. Combining Systematic and Random Error.- 11. Bias Analysis by Missing Data Methods.- 12. Bias Analysis by Empirical Methods.- 13. Bias Analysis by Bayesian Methods.- 14. Multiple Bias Modeling.- 15. Good Practices for Quantitative Bias Analysis.- 15. Presentation and Inference.- References.- Index.

Editorial Reviews

From the reviews:

"This is the first book to focus on a compilation of bias analysis methods from the epidemiologic perspective. … Throughout this well-written book, examples presented are highly informative and easy to follow for the target audience of students and public health researchers with a foundation in epidemiologic study design and methods. … this book can be used either as a reference work by practicing epidemiologists or as a textbook for an intermediate-to-advanced course in epidemiologic methods." (Chanelle J. Howe and Stephen R. Cole, American Journal of Epidemiology, Vol. 170 (10), November, 2009)

“Applying Quantitative Bias Analysis to Epidemiologic Data is the first text of its kind to give a comprehensive overview of the field. ..This book fills an important gap among epidemiology texts. It provides a unified reference for the myriad of bias analysis methods that appear in the literature. It is broad and thorough in scope, and yet easily accessible…” (Biometrics)

From the Publisher