Multi-view Multivariate Mediation Analysis with Applications to Alzheimer's Disease

Music City Center 

Many biomedical studies generate data from multiple sources or views with a 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 through 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. We propose Multi-view Multivariate Mediation Analysis (MMM), which considers both 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. MMMis applied to data from the ADNI study to explore underlying mechanisms of Alzheimer's Disease.

Multimodal data integration

Causal mediation analysis

high dimensional analysis

variable selection

multiview learning