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Data Sources for Bias Analysis

  • Timothy L. Lash
  • Aliza K. Fink
  • Matthew P. Fox
Chapter
Part of the Statistics for Biology and Health book series (SBH)

Abstract

All bias analyses modify a conventional estimate of association to account for bias introduced by systematic error. These quantitative modifications revise the conventional estimate of association (e.g., a risk difference or a rate ratio) with equations that adjust it for the estimated impact of the systematic error. These equations have parameters, called bias parameters, that ultimately determine the direction and magnitude of the adjustment. For example:
  • The proportions of all eligible subjects who participate in a study, simultaneously stratified into subgroups of persons with and without the disease outcome and within categories of the exposure variable of interest, are bias parameters. These parameters determine the direction and magnitude of selection bias.

  • The sensitivity and specificity of exposure classification, within subgroups of persons with and without the disease outcome of interest, are bias parameters that affect the direction and magnitude of bias introduced by exposure misclassification.

  • The strength of association between an unmeasured confounder and the exposure of interest and between the unmeasured confounder and the disease outcome of interest are bias parameters that affect the direction and magnitude of bias introduced by an unmeasured confounder.

Keywords

Blood Alcohol Concentration Lung Cancer Mortality Unmeasured Confounder Exercise Habit Disease Occurrence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Timothy L. Lash
    • 1
  • Aliza K. Fink
    • 1
  • Matthew P. Fox
    • 1
  1. 1.Boston University School of Public HealthBostonUSA

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