Skip to main content
  • Book
  • © 2009

Applying Quantitative Bias Analysis to Epidemiologic Data

  • Collects and synthesizes methods for quantifiying systematic errors that affect observational epidemiologic research

  • Includes supplementary material: sn.pub/extras

Part of the book series: Statistics for Biology and Health (SBH)

Buying options

eBook USD 109.00
Price excludes VAT (USA)
  • ISBN: 978-0-387-87959-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book USD 139.99
Price excludes VAT (USA)

This is a preview of subscription content, access via your institution.

Table of contents (10 chapters)

  1. Front Matter

    Pages i-xii
  2. Introduction, Objectives, and an Alternative

    • Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 1-12
  3. A Guide to Implementing Quantitative Bias Analysis

    • Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 13-32
  4. Data Sources for Bias Analysis

    • Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 33-41
  5. Selection Bias

    • Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 43-57
  6. Unmeasured and Unknown Confounders

    • Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 59-78
  7. Misclassification

    • Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 79-108
  8. Multidimensional Bias Analysis

    • Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 109-116
  9. Probabilistic Bias Analysis

    • Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 117-150
  10. Multiple Bias Modeling

    • Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 151-173
  11. Presentation and Inference

    • Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 175-181
  12. Back Matter

    Pages 183-192

About this book

This text provides the first-ever compilation of bias analysis methods for use with epidemiologic data. It guides the reader 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 classification errors. Subsequent chapters extend these methods to multidimensional bias analysis, probabilistic bias analysis, and multiple bias analysis. The text concludes with a chapter on presentation and interpretation of bias analysis results.

Although techniques for bias analysis have been available for decades, these methods are considered difficult to implement. This text not only gathers the methods into one cohesive and organized presentation, it also explains the methods in a consistent fashion and provides customizable spreadsheets to implement the solutions. By downloading the spreadsheets (available at links provided in the text), readers can follow the examples in the text and then modify the spreadsheet to complete their own bias analyses. Readers without experience using quantitative bias analysis will be able to design, implement, and understand bias analyses that address the major threats to the validity of epidemiologic research. More experienced analysts will value the compilation of bias analysis methods and links to software tools that facilitate their projects.

Timothy L. Lash is an Associate Professor of Epidemiology and Matthew P. Fox is an Assistant Professor in the Center for International Health and Development, both at the Boston University School of Public Health. Aliza K. Fink is a Project Manager at Macro International in Bethesda, Maryland. Together they have organized and presented many day-long workshops on the methods of quantitative bias analysis. In addition, they have collaborated on many papers that developed methods of quantitative bias analysis or used the methods in the data analysis.

Keywords

  • Epidemiologic Research
  • Master Patient Index
  • Monte Carlo analysis
  • bias analysis
  • classification
  • epidemiological data analysis
  • sensitivity analysis
  • infectious diseases

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)

Authors and Affiliations

  • School of Public Health, Boston University, Boston, U.S.A.

    Timothy L. Lash, Matthew P. Fox, Aliza K. Fink

Bibliographic Information

Buying options

eBook USD 109.00
Price excludes VAT (USA)
  • ISBN: 978-0-387-87959-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book USD 139.99
Price excludes VAT (USA)