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Literature DB >> 17435441

The identification of synergism in the sufficient-component-cause framework.

Abstract

Various concepts of interaction are reconsidered in light of a sufficient-component-cause framework. Conditions and statistical tests are derived for the presence of synergism within sufficient causes. The conditions derived are sufficient but not necessary for the presence of synergism. In the context of monotonic effects, the conditions derived are closely related to effect modification on the risk difference scale; however, this is not the case without the assumption of monotonic effects.

Mesh：

Year: 2007 PMID： 17435441 DOI： 10.1097/01.ede.0000260218.66432.88

Source DB: PubMed Journal: Epidemiology ISSN： 1044-3983 Impact factor: 4.822

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Cited

71 in total

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Authors: Tyler J VanderWeele; Stijn Vansteelandt
Journal: Am J Epidemiol Date: 2011-10-19 Impact factor: 4.897

2. Recommendations for presenting analyses of effect modification and interaction.

Authors: Mirjam J Knol; Tyler J VanderWeele
Journal: Int J Epidemiol Date: 2012-01-09 Impact factor: 7.196

3. Attributable fractions for sufficient cause interactions.

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Journal: Int J Biostat Date: 2010-02-22 Impact factor: 0.968

4. The association and interaction analysis of metabolic syndrome and chronic kidney disease on cardiovascular autonomic neuropathy in the general Chinese population.

Authors: Jin Zhang; Nai-Jia Liu; Yan Zhang; Hong Yang; Zachary Tang
Journal: Int J Clin Exp Med Date: 2015-06-15

The identification of synergism in the sufficient-component-cause framework.

1. A weighting approach to causal effects and additive interaction in case-control studies: marginal structural linear odds models.

2. Recommendations for presenting analyses of effect modification and interaction.

3. Attributable fractions for sufficient cause interactions.

4. The association and interaction analysis of metabolic syndrome and chronic kidney disease on cardiovascular autonomic neuropathy in the general Chinese population.

5. Stress across the life course and depression in a rapidly developing population: the Guangzhou Biobank Cohort Study.

6. A mapping between interactions and interference: implications for vaccine trials.

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8. Appropriate epidemiologic methods as a prerequisite for valid study results.

9. Inference for additive interaction under exposure misclassification.

10. Gene-environment interactions in cancer epidemiology: a National Cancer Institute Think Tank report.