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Applying Quantitative Bias Analysis to Epidemiologic Data

  • Book
  • © 2009

Overview

Authors:
  1. Timothy L. Lash

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

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    You can also search for this author in PubMed   Google Scholar

  2. Matthew P. Fox

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

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    You can also search for this author in PubMed   Google Scholar

  3. Aliza K. Fink

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

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  • 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)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xii
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  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
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Keywords

About this book

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.

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

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