Literature DB >> 7327838

Biological models and statistical interactions: an example from multistage carcinogenesis.

J Siemiatycki, D C Thomas.   

Abstract

From the assessment of statistical interaction between risk factors it is tempting to infer the nature of the biologic interaction between the factors. However, the use of statistical analyses of epidemiologic data to infer biologic processes can be misleading. as an example, we consider the multistage model of carcinogenesis. Under this biologic model, it is shown, by means of simple hypothetical examples, that even if carcinogenic factors act independently, some pairs may fit an additive statistical model, some a multiplicative statistical model, and some neither. The elucidation of biological interactions by means of statistical models requires the imaginative and prudent use of inductive and deductive reasoning; it cannot be done mechanically.

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Year:  1981        PMID: 7327838     DOI: 10.1093/ije/10.4.383

Source DB:  PubMed          Journal:  Int J Epidemiol        ISSN: 0300-5771            Impact factor:   7.196


  62 in total

1.  Next generation analytic tools for large scale genetic epidemiology studies of complex diseases.

Authors:  Leah E Mechanic; Huann-Sheng Chen; Christopher I Amos; Nilanjan Chatterjee; Nancy J Cox; Rao L Divi; Ruzong Fan; Emily L Harris; Kevin Jacobs; Peter Kraft; Suzanne M Leal; Kimberly McAllister; Jason H Moore; Dina N Paltoo; Michael A Province; Erin M Ramos; Marylyn D Ritchie; Kathryn Roeder; Daniel J Schaid; Matthew Stephens; Duncan C Thomas; Clarice R Weinberg; John S Witte; Shunpu Zhang; Sebastian Zöllner; Eric J Feuer; Elizabeth M Gillanders
Journal:  Genet Epidemiol       Date:  2011-12-06       Impact factor: 2.135

2.  Gene-environment interactions in genome-wide association studies: a comparative study of tests applied to empirical studies of type 2 diabetes.

Authors:  Marilyn C Cornelis; Eric J Tchetgen Tchetgen; Liming Liang; Lu Qi; Nilanjan Chatterjee; Frank B Hu; Peter Kraft
Journal:  Am J Epidemiol       Date:  2011-12-22       Impact factor: 4.897

3.  Interaction and exposure modification: are we asking the right questions?

Authors:  Clarice R Weinberg
Journal:  Am J Epidemiol       Date:  2012-02-03       Impact factor: 4.897

4.  Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases.

Authors:  Hugues Aschard; Jinbo Chen; Marilyn C Cornelis; Lori B Chibnik; Elizabeth W Karlson; Peter Kraft
Journal:  Am J Hum Genet       Date:  2012-05-24       Impact factor: 11.025

5.  Statistical interaction in human genetics: how should we model it if we are looking for biological interaction?

Authors:  Xuefeng Wang; Robert C Elston; Xiaofeng Zhu
Journal:  Nat Rev Genet       Date:  2010-11-23       Impact factor: 53.242

6.  Inference from a multiplicative model of joint genetic effects for [corrected] ovarian cancer risk.

Authors:  Sholom Wacholder; Summer S Han; Clarice R Weinberg
Journal:  J Natl Cancer Inst       Date:  2010-12-17       Impact factor: 13.506

7.  On the relationship of sufficient component cause models with potential outcome (counterfactual) models.

Authors:  W Dana Flanders
Journal:  Eur J Epidemiol       Date:  2006-10-18       Impact factor: 8.082

8.  Commentary: thoughts on assessing evidence for gene by environment interaction.

Authors:  Clarice R Weinberg
Journal:  Int J Epidemiol       Date:  2012-05-16       Impact factor: 7.196

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

Authors:  Carolyn M Hutter; Leah E Mechanic; Nilanjan Chatterjee; Peter Kraft; Elizabeth M Gillanders
Journal:  Genet Epidemiol       Date:  2013-10-05       Impact factor: 2.135

10.  Are major behavioral and sociodemographic risk factors for mortality additive or multiplicative in their effects?

Authors:  Neil Mehta; Samuel Preston
Journal:  Soc Sci Med       Date:  2016-02-16       Impact factor: 4.634

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