However, researchers now understand that many disease outcomes are a result of the interplay and balance between multiple contributing and intermediary factors.
The examples above have demonstrated that data integration can enhance the application of the Bradford Hill Criteria in a causal analysis by: allowing for more scrutiny in study designs; providing new tools to demonstrate consistency, specificity, and plausibility of associations; integrating molecular evaluation to determine temporality and dose—response; clarifying conflicting epidemiologic findings to determine coherence; and promoting the proposal and testing of new mechanistic hypotheses.
Furthermore, individual susceptibility and synergistic or antagonistic effects of cumulative exposures can make some biological gradients even more difficult to characterize.
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For more information and examples of such models, see.
Model 3 is fully electric, so you never need to visit a gas station again.
A theory of the yielding and plastic flow of anisotropic metals.
Thus, epidemiologic study designs which ensure a temporal progression between the two measures are more persuasive in causal inference.