A typology of four notions of confounding in epidemiology

Journal of Epidemiology

Volumn 27, Issue 2, 2017, Pages 49-55

  Suzuki, Etsuji ^a   Mitsuhashi, Toshiharu ^b   Tsuda, Toshihide ^c   Yamamoto, Eiji ^d  

^a OKAYAMA UNIVERSITY   (Japan)

^b OKAYAMA UNIVERSITY HOSPITAL   (Japan)

^c OKAYAMA UNIVERSITY   (Japan)

^d OKAYAMA UNIVERSITY OF SCIENCE   (Japan)

Author keywords

Bias; Confounding; Counterfactual; Directed acyclic graphs; Response types

Indexed keywords

COMPUTER GRAPHICS; EPIDEMIOLOGY; HEALTH SERVICE; HUMAN; STATISTICAL BIAS;

BIAS (EPIDEMIOLOGY); COMPUTER GRAPHICS; CONFOUNDING FACTORS (EPIDEMIOLOGY); EPIDEMIOLOGIC METHODS; HEALTH SERVICES NEEDS AND DEMAND; HUMANS;

EID: 85013224921     PISSN: 09175040     EISSN: 13499092     Source Type: Journal    
DOI: 10.1016/j.je.2016.09.003     Document Type: Review

References (36)

1. Greenland S, Robins JM. Identifiability, exchangeability, and epidemiological confounding. Int J Epidemiol. 1986;15:413-419.
2. Greenland S, Robins JM, Pearl J. Confounding and collapsibility in causal inference. Stat Sci. 1999;14:29-46.
3. Kaufman JS, Poole C. Looking back on "causal thinking in the health sciences". Annu Rev Public Health. 2000;21:101-119.
4. Greenland S, Morgenstern H. Confounding in health research. Annu Rev Public Health. 2001;22:189-212.
5. Maldonado G, Greenland S. Estimating causal effects. Int J Epidemiol. 2002;31: 422-429.
6. Hernán MA. A definition of causal effect for epidemiological research. J Epidemiol Community Health. 2004;58:265-271.
7. Greenland S, Rothman KJ, Lash TL. Measures of effect and measures of association. In: Rothman KJ, Greenland S, Lash TL, eds. Modern Epidemiology. Philadelphia, PA: Lippincott Williams & Wilkins; 2008:51-70.
8. Weisberg HI. Bias and Causation: Models and Judgment for Valid Comparisons. Hoboken, NJ: Wiley; 2010.
9. Hernán MA. Beyond exchangeability: the other conditions for causal inference in medical research. Stat Methods Med Res. 2012;21:3-5.
10. VanderWeele TJ, Robins JM. The identification of synergism in the sufficientcomponent-cause framework. Epidemiology. 2007;18:329-339.
11. Suzuki E, Yamamoto E, Tsuda T. On the link between sufficient-cause model and potential-outcome model. Epidemiology. 2011;22:131-132.
12. VanderWeele TJ. Confounding and effect modification: distribution and measure. Epidemiol Method. 2012 Aug;1(1):55-82 [cited 2012 Aug 29] Available from: http://www.degruyter.com/view/j/em.2012.1.issue-1/2161-962X.1004/2161-962X.1004.xml?format=INT.
13. Suzuki E, Yamamoto E. Further refinements to the organizational schema for causal effects. Epidemiology. 2014;25:618-619.
14. Greenland S. Confounding. In: Boslaugh S, ed. Encyclopedia of Epidemiology. Thousand Oaks, CA: Sage Publications; 2008:227-232.
15. Gatto NM, Campbell UB, Schwartz S. An organizational schema for epidemiologic causal effects. Epidemiology. 2014;25:88-97.
16. Maldonado G. Toward a clearer understanding of causal concepts in epidemiology. Ann Epidemiol. 2013;23:743-749.
17. Greenland S, Robins JM. Identifiability, exchangeability and confounding revisited. Epidemiol Perspect Innov. 2009 Sep;vol. 6 [4 p.] [cited 2009 Sep 4] Available from: http://archive.biomedcentral.com/1742-5573/6/4.
18. Porta MS, ed. A Dictionary of Epidemiology. sixth ed. New York: NY. Oxford University Press; 2014.
19. Everitt B, Skrondal A. The Cambridge Dictionary of Statistics. fourth ed. Cambridge: UK. Cambridge University Press; 2010.
20. Upton G, Cook I. A Dictionary of Statistics. second ed. New York, NY: Oxford University Press; 2008.
21. Greenland S. Randomization, statistics, and causal inference. Epidemiology. 1990;1:421-429.
22. Schwartz S, Campbell UB, Gatto NM, Gordon K. Toward a clarification of the taxonomy of "bias" in epidemiology textbooks. Epidemiology. 2015;26: 216-222.
23. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10:37-48.
24. Glymour MM, Greenland S. Causal diagram. In: Rothman KJ, Greenland S, Lash TL, eds. Modern Epidemiology. Philadelphia, PA: Lippincott Williams & Wilkins; 2008:183-209.
25. Robins JM. Data, design, and background knowledge in etiologic inference. Epidemiology. 2001;12:313-320.
26. Hernán MA, Hernández-Díaz S, Werler MM, Mitchell AA. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. Am J Epidemiol. 2002;155:176-184.
27. VanderWeele TJ, Hernán MA, Robins JM. Causal directed acyclic graphs and the direction of unmeasured confounding bias. Epidemiology. 2008;19:720-728.
28. Shrier I, Platt RW. Reducing bias through directed acyclic graphs. BMC Med Res Methodol. 2008 Oct;8 [70 p.] [cited 2008 Oct 30] Available from: www. biomedcentral.com/1471-2288/8/70.
29. Suzuki E, Mitsuhashi T, Tsuda T, Yamamoto E. A counterfactual approach to bias and effect modification in terms of response types. BMC Med Res Methodol. 2013 July;13 [101 p.] [cited 2013 July 31] Available from http://www. biomedcentral.com/1471-2288/13/101.
30. Suzuki E. Generalized causal measure: the beauty lies in its generality. Epidemiology. 2015;26:490-495.
31. Suzuki E, Mitsuhashi T, Tsuda T, Yamamoto E. A simple example as a pedagogical device? Ann Epidemiol. 2014;24:560-561.
32. Hernán MA, VanderWeele TJ. Compound treatments and transportability of causal inference. Epidemiology. 2011;22:368-377.
33. Petersen ML. Compound treatments, transportability, and the structural causal model: the power and simplicity of causal graphs. Epidemiology. 2011;22: 378-381.
34. Hoggatt KJ, Greenland S. Extending organizational schema for causal effects. Epidemiology. 2014;25:98-102.
35. Petersen ML, van der Laan MJ. Causal models and learning from data: integrating causal modeling and statistical estimation. Epidemiology. 2014;25: 418-426.
36. Borkar VS. There's no such thing as a free lunch: the bias-variance dilemma. Resonance. 1998;3:40-51.