My group in ‘Integrative Epidemiology’ combines excellence in study design with state of the art genomic analyses. This is applied across cancer research into causation, prevention, and prognosis. This will lead to the development of causal analysis methods for application in population-based and clinical health sciences.
The goal of integrative epidemiology is to accelerate the translation of scientific discoveries to population health impact. Integration of the emerging wealth of data–including “omics” data, epidemiologic and clinical data, and behavioral and geographical data–will lead to the construction of “system” models. These models will enhance our understanding of the mechanisms underlying cancer risk and the macro-level factors (e.g., social determinants and health care policy) that may influence outcomes, generate hypotheses for future research, and identify possible targets for prevention or clinical intervention.
“Integrative epidemiology” combines epidemiologic study design with rapid advances in analytic systems and biostatistical and bioinformatic tools in order to extend the boundaries of molecular epidemiology. In order to facilitate this integrative approach, it is necessary to:
- Promote a model of research that is cross-disciplinary and collaborative. Shared data and related biospecimens are essential to support this approach.
- Develop systematic approaches to manage and display complex datasets.
- Coalesce and mine data from disparate sources, using the tools developed by the field of data science.
- Develop approaches for synthesis and translation of multi-level information within the framework of cancer epidemiological research.
Affiliations & Activities
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