Sufficient cause interactions and statistical interactions

Epidemiology

Volumn 20, Issue 1, 2009, Pages 6-13

  Vanderweele, Tyler J ^a  

^a UNIVERSITY OF CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CONFOUNDING VARIABLE; LOGISTIC REGRESSION ANALYSIS; LOGLINEAR MODEL; MATHEMATICAL COMPUTING; MEDICAL RESEARCH; OUTCOME ASSESSMENT; PRIORITY JOURNAL; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; CAUSALITY; DATA INTERPRETATION, STATISTICAL; EPIDEMIOLOGIC STUDIES;

EID: 67349266632     PISSN: 10443983     EISSN: None     Source Type: Journal    
DOI: 10.1097/EDE.0b013e31818f69e7     Document Type: Article

References (25)

1. Siemiatycki J, Thomas DC. Biological models and statistical interactions: an example from multistage carcinogenesis. Int J Epidemiol. 1981;10:383-387.
2. Thompson WD. Effect modification and the limits of biological inference from epidemiologic data. J Clin Epidemiol. 1991;44:221-232.
3. Rothman KJ, Greenland S. Modern Epidemiology. 2nd ed. Philadelphia: Lippincott-Raven; 1998.
4. VanderWeele TJ, Robins JM. The identification of synergism in the sufficient- component cause framework. Epidemiology. 2007;18:329-339.
5. VanderWeele TJ, Robins JM. Empirical and counterfactual conditions for sufficient cause interactions. Biometrika. 2008;95:49-61.
6. Rothman KJ. Causes. Am J Epidemiol. 1976;104:587-592.
7. Greenland S, Poole C. Invariants and noninvariants in the concept of interdependent effects. Scand. J Work Environ Health. 1988;14:125-129.
8. VanderWeele TJ, Robins JM. Minimal sufficient causation and directed acyclic graphs. Ann Statist. In press.
9. Greenland S, Brumback B. An overview of relations among causal modelling methods. Int J Epidemiol. 2002;31:1030 -1037.
10. Koopman JS. Interaction between discrete causes. Am J Epidemiol. 1981;113:716 -724.
11. Darroch JN, Borkent M. Synergism, attributable risk and interaction for two binary exposure factors. Biometrika. 1994;81:259 -270.
12. Rothman KJ. Modern Epidemiology. 1st ed. Boston, MA: Little, Brown and Company; 1986.
13. Hosmer DW, Lemeshow S. Confidence interval estimation of interaction. Epidemiology. 1992;3:452-456.
14. Assman SF, Hosmer DW, Lemeshow S, et al. Confidence intervals for measures of interaction. Epidemiology. 1996;7:286 -290.
15. Knol MJ, van der Tweel I, Grobbee DE, et al. Estimating interaction on an additive scale between continuous determinants in a logistic regression model. Int J Epidemiol. 2007;36:1111-1118.
16. Rothman KJ, Greenland S, Lash TL. Modern Epidemiology. 3rd ed. Philadelphia: Lippincott Williams & Wilkins; 2008.
17. Wacholder S. Binomial regression in GLIM: estimating risk ratios and risk differences. Am J Epidemiol. 1986;123:174 -184.
18. Vansteelandt S, VanderWeele TJ, Robins JM. Multiply robust inference for statistical interactions. J Am Statist Assoc. In press.
19. Greenland S. Estimating standardized parameters from generalized linear models. Stat Med. 1991;10:1069 -1074.
20. Greenland S. Model-based estimation of relative risks and other epidemiologic measures in studies of common outcomes and in case-control studies. Am J Epidemiol. 2004;160:301-305.
21. Spiegelman D, Hertzmark E. Easy SAS calculations for risk or prevalence ratios and differences. Am J Epidemiol. 2005;162:199 -200.
22. Zou G. A modified Poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159:702-706.
23. Skrondal A. Interaction as departure from additivity in case-control studies: a cautionary note. Am J Epidemiol. 2003;158:251-258.
24. Cheung YB. A modified least-squares regression approach to the estimation of risk difference. Am J Epidemiol. 2007;166:1337-1344.
25. Lumley T, Kronmal R, Ma S. Relative risk regression in medical research: models, contrasts, estimators, and algorithms. UW Biostatistics Working Paper Series. Working Paper 293. Available at: http://www.bepress.com/uwbiostat/ paper293.