Collider bias is only a partial explanation for the obesity paradox

Epidemiology

Volumn 27, Issue 4, 2016, Pages 525-530

  Sperrin, Matthew ^a   Candlish, Jane ^a   Badrick, Ellena ^a   Renehan, Andrew ^a   Buchan, Iain ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; DISEASE ASSOCIATION; HUMAN; INVESTIGATIVE PROCEDURES; MORTALITY; OBESITY; OUTCOME ASSESSMENT; PRIORITY JOURNAL; EPIDEMIOLOGY; STATISTICAL BIAS; STATISTICAL MODEL;

BIAS; CONFOUNDING FACTORS (EPIDEMIOLOGY); HUMANS; LOGISTIC MODELS; MORTALITY; OBESITY;

EID: 84963701445     PISSN: 10443983     EISSN: 15315487     Source Type: Journal    
DOI: 10.1097/EDE.0000000000000493     Document Type: Article

References (21)

1. Romero-Corral A, Montori VM, Somers VK, et al. Association of bodyweight with total mortality and with cardiovascular events in coronary artery disease: a systematic review of cohort studies. Lancet. 2006;368:666-678.
2. Oreopoulos A, Padwal R, Kalantar-Zadeh K, Fonarow GC, Norris CM, McAlister FA. Body mass index and mortality in heart failure: a metaanalysis. Am Heart J. 2008;156:13-22.
3. Carnethon MR, De Chavez PJ, Biggs ML, et al. Association of weight status with mortality in adults with incident diabetes. JAMA. 2012;308:581-590.
4. Zhao W, Katzmarzyk PT, Horswell R, et al. Body mass index and the risk of all-cause mortality among patients with type 2 diabetes. Circulation. 2014; 130: 2143-2151.
5. 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.
6. Rothman KJ. BMI-related errors in the measurement of obesity. Int J Obes (Lond). 2008;32(suppl 3):S56-S59.
7. Dixon JB, Egger GJ, Finkelstein EA, Kral JG, Lambert GW. Obesity paradox misunderstands the biology of optimal weight throughout the life cycle. Int J Obes (Lond). 2015;39:82-84.
8. Preston SH, Stokes A. Obesity paradox: conditioning on disease enhances biases in estimating the mortality risks of obesity. Epidemiology. 2014;25:454-461.
9. Cole SR, Platt RW, Schisterman EF, et al. Illustrating bias due to conditioning on a collider. Int J Epidemiol. 2010;39:417-420.
10. Pearl J. Causality Models, Reasoning and Inference. 2nd ed. Cambridge University Press; 2009. London, UK.
11. Hernan MA, Robins JM. Causal Inference. Boca Raton, FL: Chapman & Hall/CRC, forthcoming; 2016.
12. VanderWeele TJ. Bias formulas for sensitivity analysis for direct and indirect effects. Epidemiology. 2010;21:540-551.
13. RCore Team. R: A Language and Environment for Statistical Computing. 2014.
14. Glymour MM, Vittinghoff E. Commentary: selection bias as an explanation for the obesity paradox: just because it's possible doesn't mean it's plausible. Epidemiology. 2014;25:4-6.
15. Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology. 2003;14:300-306.
16. Banack HR, Kaufman JS. Does selection bias explain the obesity paradox among individuals with cardiovascular disease? Ann Epidemiol. 2015;25:342-349.
17. Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-Analysis. JAMA 2013;309:71-82.
18. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10:37-48.
19. Greenland S, Mansournia MA. Limitations of individual causal models, causal graphs, and ignorability assumptions, as illustrated by random confounding and design unfaithfulness. Eur J Epidemiol. 2015;30:1101-1110.
20. Lajous M, Bijon A, Fagherazzi G, et al. Body mass index, diabetes, and mortality in French women: explaining away a paradox. Epidemiology. 2014;25:10-14.
21. Berzuini C, Dawid AP. Deep determinism and the assessment of mechanistic interaction. Biostatistics. 2013;14:502-513.