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Multiple bias analysis using logistic regression: an example from the National Birth Defects Prevention Study.

Candice Y Johnson1, Penelope P Howards2, Matthew J Strickland3

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|June 25, 2018
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Summary
This summary is machine-generated.

This study developed a logistic regression method to adjust for multiple biases, including exposure misclassification and selection bias, in epidemiologic research. The findings suggest that the association between obesity and cleft lip/palate may be influenced by these biases.

Keywords:
BiasBody mass indexCleft lipRegression analysis

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Epidemiologic studies face challenges from biases like exposure misclassification, selection bias, and confounding.
  • Quantitative adjustment for confounding is common, but methods for multiple biases are less established.

Purpose of the Study:

  • To describe a novel approach combining existing methods to adjust for multiple biases simultaneously using logistic regression.
  • To apply this method to a case-control study investigating prepregnancy obesity and cleft lip with or without cleft palate (CL/P).

Main Methods:

  • Developed a method integrating two techniques to adjust for exposure misclassification, selection bias, and confounding.
  • Applied multivariable logistic regression with created weights reflecting selection probabilities and exposure classification accuracy.
  • Utilized data from the National Birth Defects Prevention Study, including 2523 cases and 10,605 controls.

Main Results:

  • Initial analysis adjusting only for confounding showed a weak association between prepregnancy obesity and CL/P (OR: 1.10).
  • Multiple bias-adjusted odds ratios ranged from 0.93 to 1.03 after accounting for exposure misclassification, missing data, selection bias, and confounding.
  • Probabilistic and nonprobabilistic analyses yielded similar bias-adjusted results.

Conclusions:

  • The developed logistic regression approach effectively adjusts for multiple biases in epidemiologic studies.
  • The observed association between maternal obesity and CL/P may be attributable to unaddressed biases.
  • This method offers a quantitative way to address complex biases beyond confounding.