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.
References
Glymour MM, Greenland S. Causal Diagrams. In: Rothman KJ, Greenland S, Lash T, editors. Modern epidemiology. 3rd ed. Philadelphia: Lippincott Williams Wilkins; 2008. p. 183–212.
Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15(5):615–25.
Banack HR, Kaufman JS. The obesity paradox: understanding the effect of obesity on mortality among individuals with cardiovascular disease. Prev Med. 2014;62:96–102.
Preston SH, Stokes A. Obesity paradox: conditioning on disease enhances biases in estimating the mortality risks of obesity. Epidemiology. 2014;25(3):454–61.
Flanders DW, Eldridge RC, McClellan W. A nearly unavoidable mechanism for collider bias in the obesity-end-stage-renal-disease-mortality and similar studies. Epidemiology. 2014;25(5):762–4.
The BMI in Diverse Populations Collaborative Group. Effect of Smoking on the body mass index-mortality relation: empirical evidence from 15 studies. Am J Epidemiol. 1999;150(12):1297–308.
Chiolero A, Faeh D, Paccaud F, Cornuz J. Consequences of smoking for body weight, body fat distribution, and insulin resistance. Am J Clin Nutr. 2008;87(4):801–9.
Durazo-Arvizu RA, Cooper RS. Issues related to modeling the body mass index-mortality association: the shape of the association and the effects of smoking status. Int J Obes. 2008;32(S3):S52–5.
Mehio-Sibai A, Feinleib M, Sibai TA, Armenian HK. a positive or a negative confounding variable? A simple teaching aid for clinicians and students. Ann Epidemiol. 2005;15(6):421–3.
VanderWeele TJ, Robins JM. Signed directed acyclic graphs for causal inference. J R Stat Soc Ser B Stat Methodol. 2010;72(1):111–27.
de Gonzalez Berrington, et al. Body-mass index and mortality among 1.46 million white adults. N Engl J Med. 2010;363(23):2211–9.
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Banack, H.R., Kaufman, J.S. From bad to worse: collider stratification amplifies confounding bias in the “obesity paradox”. Eur J Epidemiol 30, 1111–1114 (2015). https://doi.org/10.1007/s10654-015-0069-7
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DOI: https://doi.org/10.1007/s10654-015-0069-7