Applying Quantitative Bias Analysis to Epidemiologic Data

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

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

Table of contents

  1. Front Matter
    Pages i-xii
  2. Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 1-12
  3. Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 13-32
  4. Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 33-41
  5. Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 43-57
  6. Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 59-78
  7. Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 79-108
  8. Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 109-116
  9. Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 117-150
  10. Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 151-173
  11. Timothy L. Lash, Aliza K. Fink, Matthew P. Fox
    Pages 175-181
  12. Back Matter
    Pages 183-192

About this book

Introduction

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

Authors and affiliations

  • Timothy L. Lash
    • 1
  • Matthew P. Fox
    • 2
  • Aliza K. Fink
    • 3
  1. 1.School of Public HealthBoston UniversityBostonU.S.A.
  2. 2.School of Public HealthBoston UniversityBostonU.S.A.
  3. 3.School of Public HealthBoston UniversityBostonU.S.A.

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-87959-8
  • Copyright Information Springer-Verlag New York 2009
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-0-387-87960-4
  • Online ISBN 978-0-387-87959-8
  • Series Print ISSN 1431-8776
  • About this book