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Potential sensitivity of bias analysis results to incorrect assumptions of nondifferential or differential binary exposures misclassification

Filetype[PDF-286.83 KB]


  • English
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    Details:

    • Alternative Title:
      Epidemiology
    • Personal Author:
    • Description:
      Background

      Results of bias analyses for exposure misclassification are dependent on assumptions made during analysis. We describe how adjustment for misclassification is affected by incorrect assumptions about whether sensitivity and specificity are the same (nondifferential) or different (differential) for cases and non-cases.

      Methods

      We adjusted for exposure misclassification using probabilistic bias analysis, under correct and incorrect assumptions about whether exposure misclassification was differential or not. First, we used simulated datasets in which nondifferential and differential misclassification were introduced. Then, we used data on obesity and diabetes from the National Health and Nutrition Examination Survey (NHANES) in which both self-reported (misclassified) and measured (true) obesity were available, using literature estimates of sensitivity and specificity to adjust for bias. The ratio of odds ratio (ROR; observed odds ratio divided by true odds ratio) was used to quantify magnitude of bias, with ROR=1 signifying no bias.

      Results

      In the simulated datasets, under incorrect assumptions (e.g., assuming nondifferential misclassification when it was truly differential), results were biased, with RORs ranging from 0.18 to 2.46. In NHANES, results adjusted based on incorrect assumptions also produced biased results, with RORs ranging from 1.26 to 1.55; results were more biased when making these adjustments than when using the misclassified exposure values (ROR=0.91).

      Conclusions

      Making an incorrect assumption about nondifferential or differential exposure misclassification in bias analyses can lead to more biased results than if no adjustment is performed. In our analyses, incorporating uncertainty using probabilistic bias analysis was not sufficient to overcome this problem.

    • Subjects:
    • Source:
      Epidemiology. 25(6):902-909.
    • Pubmed ID:
      25120106
    • Pubmed Central ID:
      PMC4477528
    • Document Type:
      Journal Article
    • Funding:
      CC999999/Intramural CDC HHS/United States
    • Place as Subject:
      United States
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