The objective of the proposed research is to develop theory and methods for testing for sufficient cause inter- actions. The methods will be useful in identifying mechanistic interactions in biological systems and in and in the analysis and interpretation of studies in genetic epidemiology of gene-gene and gene-environment interactions. It is well known both that the presence of an interaction in a statistical model depends on the model being employed and furthermore that a statistical interaction need not correspond to an interaction in any biologically or physically meaningful sense. The sufficient cause framework makes reference to the actual causal mechanisms, referred to as sufficient causes, involved in bringing about the outcome. When two or more binary causes participate in the same causal mechanism, synergism is said to be present. Sometimes synergism cannot be identified from data;when data do imply that synergism must be present then a sufficient cause interaction is said to be present. The theory and methods developed through the proposed research lead to empirical tests for sufficient cause interactions and thus constitute tests for the joint presence of two or more causes in a single causal mechanism.
The aims of the research are to extend the theory concerning the sufficient cause framework for dichotomous exposures, to develop theory for sufficient cause interaction for ordinal and categorical exposures, to develop multiply robust semiparametric tests for the presence of sufficient cause interactions, and to characterize those forms of exposure misclassification for which tests for sufficient cause interactions yield valid conclusions. The research will provide a set of techniques that can be used to identify mechanistic interactions in biological systems and will develop both a theoretical framework in which to conceptualize these mechanistic interactions and provide methods to empirically test for such interactions. The research will be useful in identifying mechanistic gene-gene and gene-environment interactions which could increase our understanding of genetic mechanisms. The implications of the research on sufficient cause interactions for understanding the mechanistic implications of standard gene-gene and gene-environment interaction tests and study designs will be explored and the methods developed will be applied to several data sets in the Health Effects of Arsenic Longitudinal Study. The research will make important advances to the statistical literature on the concept of interaction and on the implications of measurement error for causal inference. The overall research program will contribute to our understanding of the concepts of causation which form the foundation of the statistical literature in causal inference and which are being employed in medicine, epidemiology, psychology, genetics, computer science, philosophy, sociology, education and economics.

Public Health Relevance

The statistical methodology developed in the proposed research will be useful in identifying mechanistic interactions in biological systems and in the analysis and interpretation of studies in genetic epidemiology of gene-gene and gene-environment interactions. The methods will be applied to data in the Health Effects of Arsenic Longitudinal Study in order to provide knowledge about the underlying pathophysiology and mechanisms by which arsenic exposure may lead to diseases. The research will make important advances to the statistical literature on the concept of interaction and on the implications of measurement error for causal inference.

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Research Project (R01)
Project #
5R01ES017876-05
Application #
8607940
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Mcallister, Kimberly A
Project Start
2010-04-01
Project End
2015-01-31
Budget Start
2014-02-01
Budget End
2015-01-31
Support Year
5
Fiscal Year
2014
Total Cost
$245,617
Indirect Cost
$79,279
Name
Harvard University
Department
Public Health & Prev Medicine
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
VanderWeele, Tyler J; Tchetgen Tchetgen, Eric J; Cornelis, Marilyn et al. (2014) Methodological challenges in mendelian randomization. Epidemiology 25:427-35
VanderWeele, Tyler J; Robinson, Whitney R (2014) Rejoinder: how to reduce racial disparities?: Upon what to intervene? Epidemiology 25:491-3
Suzuki, Etsuji; Evans, David; Chaix, Basile et al. (2014) On the "proportion eliminated" for risk differences versus excess relative risks. Epidemiology 25:309-10
VanderWeele, Tyler J (2014) Commentary: Resolutions of the birthweight paradox: competing explanations and analytical insights. Int J Epidemiol 43:1368-73
VanderWeele, Tyler J; Robinson, Whitney R (2014) On the causal interpretation of race in regressions adjusting for confounding and mediating variables. Epidemiology 25:473-84
VanderWeele, Tyler J (2014) A unification of mediation and interaction: a 4-way decomposition. Epidemiology 25:749-61
Jackson, John W; VanderWeele, Tyler J; Viswanathan, Anand et al. (2014) The explanatory role of stroke as a mediator of the mortality risk difference between older adults who initiate first- versus second-generation antipsychotic drugs. Am J Epidemiol 180:847-52
Valeri, Linda; Vanderweele, Tyler J (2014) The estimation of direct and indirect causal effects in the presence of misclassified binary mediator. Biostatistics 15:498-512
VanderWeele, Tyler J; Vansteelandt, Stijn (2014) Invited commentary: Some advantages of the relative excess risk due to interaction (RERI)--towards better estimators of additive interaction. Am J Epidemiol 179:670-1
Taylor, Amy E; Davies, Neil M; Ware, Jennifer J et al. (2014) Mendelian randomization in health research: using appropriate genetic variants and avoiding biased estimates. Econ Hum Biol 13:99-106

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