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

Purpose: Exposure misclassification, selection bias, and confounding are important biases in epidemiologic studies, yet only confounding is routinely addressed quantitatively. We describe how to combine two previously described methods and adjust for multiple biases using logistic regression. Methods: Weights were created from selection probabilities and predictive values for exposure classification and applied to multivariable logistic regression models in a case -control study of prepregnancy obesity (body mass index >= 30 vs. <30 kg/m(2)) and cleft lip with or without cleft palate (CLIP) using data from the National Birth Defects Prevention Study (2523 cases, 10,605 controls). Results: Adjusting for confounding by race/ethnicity, prepregnancy obesity, and CL/P were weakly associated (odds ratio [OR]: 1.10; 95% confidence interval: 0.98,1.23). After weighting the data to account for exposure misclassification, missing exposure data, selection bias, and confounding, multiple bias adjusted ORs ranged from 0.94 to 1.03 in nonprobabilistic bias analyses and median multiple bias adjusted ORs ranged from 0.93 to 1.02 in probabilistic analyses. Conclusions: This approach, adjusting for multiple biases using a logistic regression model, suggested that the observed association between obesity and CL/P could be due to the presence of bias. Published by Elsevier Inc.