A0994
Title: Multi-view multivariate mediation analysis
Authors: Sandra Safo - University of Minnesota (United States) [presenting]
Abstract: Many biomedical studies generate data from multiple sources or views with the main goal of integrating these diverse but complementary data for deeper biological insights. Most existing integrative analysis methods only consider associations among the views and an outcome without inferring potential causal relationships. Mediation analysis explores causal relationships between exposures and an outcome by including a mediator as an intermediate variable. Existing mediation analysis methods consider only single variate and single view exposures, and none incorporate multi-view exposures. Multi-view multivariate mediation analysis (MMM) is proposed, which considers both high-dimensional multivariate exposures and mediators and incorporates multi-view exposures. MMM integrates multi-view exposures by identifying disentangled common drivers accounting for indirect effects via a multivariate mediator and direct effects to be estimated separately. Simulation studies are used to demonstrate the effectiveness of MMM in comparison with other methods. MMM is applied to data from the ADNI study to explore the potential causal relationship between multiomics exposures and Alzheimer's Disease progression via genetic mediators.
