Triangulating Evidence through the Inclusion of Genetically Informed Designs

Summary

Much research effort is invested in attempting to determine causal influences on disease onset and progression to inform prevention and treatment efforts. However, this is often dependent on observational data that are prone to well-known limitations, particularly residual confounding and reverse causality. Several statistical methods have been developed to support stronger causal inference. However, a complementary approach is to use design-based methods for causal inference, which acknowledge sources of bias and attempt to mitigate these through the design of the study rather than solely through statistical adjustment. Genetically informed methods provide a novel and potentially powerful extension to this approach, accounting by design for unobserved genetic and environmental confounding