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

• Type of Material: Article
• Authors: Candice Y. Johnson, Emory University; Penelope Howards, Emory University; Matthew Strickland, Emory University; D. Kim Waller, University of Texas Houston; W Dana Flanders, Emory University
• Language: English
• Date: 2018-08-01
• Publisher: Elsevier: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: Published by Elsevier Inc. CC BY NC ND 4.0
• Final Published Version (URL): http://dx.doi.org/10.1016/j.annepidem.2018.05.009
• Title of Journal or Parent Work: Annals of Epidemiology
• ISSN: 1047-2797
• Volume: 28
• Issue: 8
• Start Page: 510
• End Page: 514
• Grant/Funding Information: This work was supported by the Centers for Disease Control and Prevention (cooperative agreements PA 96043, PA 02081, and FOA DD09–001 to the Centers for Birth Defects Research and Prevention participating in the National Birth Defects Prevention Study).
• Keywords: Cleft lip; ASSUMPTIONS; MATERNAL OBESITY; METAANALYSIS; Science & Technology; Regression analysis; RISK; SENSITIVITY-ANALYSIS; OROFACIAL CLEFTS; MISCLASSIFICATION; Body mass index; Public, Environmental & Occupational Health; Life Sciences & Biomedicine; ERRORS; Bias
• Research Categories: Health Sciences, Public Health; Health Sciences, Epidemiology

Abstract

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 (CL/P) 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.