From bad to worse: collider stratification amplifies confounding bias in the "obesity paradox"
- PMID: 26187718
- DOI: 10.1007/s10654-015-0069-7
From bad to worse: collider stratification amplifies confounding bias in the "obesity paradox"
Abstract
Smoking is often identified as a confounder of the obesity-mortality relationship. Selection bias can amplify the magnitude of an existing confounding bias. The objective of the present report is to demonstrate how confounding bias due to cigarette smoking is increased in the presence of collider stratification bias using an empirical example and directed acyclic graphs. The empirical example uses data from the Atherosclerosis Risk in Communities (ARIC) study, a prospective cohort study of 15,792 men and women in the United States. Poisson regression models were used to examine the confounding effect of smoking. In the total ARIC study population, smoking produced a confounding bias of <3 percentage points. This result was obtained by comparing the incidence rate ratio (IRR) for obesity from a model adjusted for smoking was 1.07 (95 % CI 1.00, 1.15) with one that did not adjust for smoking was 1.10 (95 % CI 1.03, 1.18). However, among smokers with CVD, the obesity IRR was 0.89 (95 % CI 0.81, 0.99), while among non-smokers with CVD the obesity IRR was 1.20 (95 % CI 1.03, 1.41). The empirical and graphical explanations presented suggest that the magnitude of the confounding bias induced by smoking is greater in the presence of collider stratification bias.
Keywords: Confounding bias; Obesity paradox; Selection bias.
Similar articles
-
The obesity paradox: understanding the effect of obesity on mortality among individuals with cardiovascular disease.Prev Med. 2014 May;62:96-102. doi: 10.1016/j.ypmed.2014.02.003. Epub 2014 Feb 10. Prev Med. 2014. PMID: 24525165 Review.
-
Does selection bias explain the obesity paradox among individuals with cardiovascular disease?Ann Epidemiol. 2015 May;25(5):342-9. doi: 10.1016/j.annepidem.2015.02.008. Epub 2015 Feb 20. Ann Epidemiol. 2015. PMID: 25867852
-
Collider Bias Is Only a Partial Explanation for the Obesity Paradox.Epidemiology. 2016 Jul;27(4):525-30. doi: 10.1097/EDE.0000000000000493. Epidemiology. 2016. PMID: 27075676 Free PMC article.
-
Quantifying biases in causal models: classical confounding vs collider-stratification bias.Epidemiology. 2003 May;14(3):300-6. Epidemiology. 2003. PMID: 12859030
-
Introduction to causal diagrams for confounder selection.Respirology. 2014 Apr;19(3):303-11. doi: 10.1111/resp.12238. Epub 2014 Jan 22. Respirology. 2014. PMID: 24447391 Review.
Cited by
-
Predicting the presence of depressive symptoms in the HIV-HCV co-infected population in Canada using supervised machine learning.BMC Med Res Methodol. 2022 Aug 12;22(1):223. doi: 10.1186/s12874-022-01700-y. BMC Med Res Methodol. 2022. PMID: 35962372 Free PMC article.
-
The Authors Respond.Epidemiology. 2017 Sep;28(5):e46. doi: 10.1097/EDE.0000000000000692. Epidemiology. 2017. PMID: 28763346 Free PMC article. No abstract available.
-
Effects of Weight History on the Association Between Directly Measured Adiposity and Mortality in Older Adults.J Gerontol A Biol Sci Med Sci. 2019 Nov 13;74(12):1937-1943. doi: 10.1093/gerona/glz144. J Gerontol A Biol Sci Med Sci. 2019. PMID: 31168573 Free PMC article.
-
Using computable knowledge mined from the literature to elucidate confounders for EHR-based pharmacovigilance.J Biomed Inform. 2021 May;117:103719. doi: 10.1016/j.jbi.2021.103719. Epub 2021 Mar 11. J Biomed Inform. 2021. PMID: 33716168 Free PMC article.
-
History of Benzodiazepine Prescriptions and Risk of Dementia: Possible Bias Due to Prevalent Users and Covariate Measurement Timing in a Nested Case-Control Study.Am J Epidemiol. 2019 Jul 1;188(7):1228-1236. doi: 10.1093/aje/kwz073. Am J Epidemiol. 2019. PMID: 31111865 Free PMC article.
References
MeSH terms
LinkOut - more resources
Full Text Sources
Medical
Research Materials