ArticleAnnals of epidemiology2018
Multiple bias analysis using logistic regression: an example from the National Birth Defects Prevention Study.
Article in Annals of epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Assessing the Impact of Exposure Misclassification in Case-Control Studies of Self-Reported Medication Use.Paediatric and perinatal epidemiology · 2026Article
- Bias analyses to investigate the impact of differential participation: Application to a birth defects case-control study.Paediatric and perinatal epidemiology · 2024Article
- Health Equity Implications of Missing Data Among Youths With Childhood-Onset Systemic Lupus Erythematosus: A Proof-of-Concept Study in the Childhood Arthritis and Rheumatology Research Alliance Registry.Arthritis care & research · 2023Article
- Simultaneous adjustment of uncontrolled confounding, selection bias and misclassification in multiple-bias modelling.International journal of epidemiology · 2023Article
- Delayed entry into prenatal care among women with pre-pregnancy health conditions, National Birth Defects Prevention Study, 1997-2011.Preventive medicine · 2022Article
- Prepregnancy body mass index and spina bifida: Potential contributions of bias.Birth defects research · 2021Article
- Gestational Weight Gain and Long-term Maternal Obesity Risk: A Multiple-Bias Analysis.Epidemiology (Cambridge, Mass.) · 2021Article
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Authors and funding
6 authors.
Funding
Abstract
purposeExposure 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.
methodsWeights 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
resultsAdjusting 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.
conclusionsThis 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.
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