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Estimating and correcting for confounder misclassification.

D A Savitz1, A E Barón

  • 1Department of Epidemiology, School of Public Health, University of North Carolina, Chapel Hill 27599.

American Journal of Epidemiology
|May 1, 1989
PubMed
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Confounder misclassification in epidemiology reduces bias adjustment effectiveness. This study quantines residual confounding from imperfect confounder classification, offering methods for accurate odds ratio estimation.

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Confounder misclassification is a significant challenge in epidemiologic studies, similar to exposure variable misclassification.
  • Adjusting for imperfectly classified confounders incompletely removes confounding bias, particularly impacting modest exposure effects with strong confounders.

Purpose of the Study:

  • To derive and present the Per Cent Adjustment (PCA) metric for quantifying bias removal from misclassified confounders.
  • To provide methods for estimating the completely adjusted odds ratio using misclassified confounder data.

Main Methods:

  • Derivation of the Per Cent Adjustment (PCA) for various case-control study scenarios.
  • Analysis of the relationship between PCA and the sensitivity/specificity of confounder classification.
  • Development of figures for estimating adjusted odds ratios based on misclassification estimates.

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Main Results:

  • The Per Cent Adjustment (PCA) demonstrates a stable relationship with confounder classification sensitivity and specificity.
  • Provided figures enable quantitative assessment of residual confounding due to misclassification.
  • The method is illustrated using the example of coffee consumption, bladder cancer, and cigarette smoking confounding.

Conclusions:

  • Offers a quantitative method to assess the impact of residual confounding from misclassified confounders.
  • Facilitates more accurate estimation of true exposure effects in the presence of imperfect confounder data.
  • Applicable to various epidemiologic study designs and confounder types.