Technological advancements in noninvasive imaging facilitate the construction of whole brain interconnected networks, known as brain connectivity. Existing approaches to analyze brain connectivity frequently disaggregate the entire network into a vector of unique edges or summary measures, leading to a substantial loss of information. Motivated by the need to explore the effect mechanism among genetic exposure, brain connectivity and time to disease onset, we propose an integrative Bayesian framework to model the effect pathway between each of these components while quantifying the mediating role of brain networks. To accommodate the biological architectures of brain connectivity constructed along white matter fiber tracts, we develop a structural modeling framework that includes a symmetric matrix-variate accelerated failure time model and a symmetric matrix response regression to characterize the effect paths. We further impose within-graph sparsity and between-graph shrinkage to identify informative network configurations and eliminate the interference of noisy components. Extensive simulations confirm the superiority of our method compared with existing alternatives. By applying the proposed method to the landmark Alzheimer's Disease Neuroimaging Initiative study, we obtain neurobiologically plausible insights that may inform future intervention strategies.

Composite endpoints consisting of both terminal and non-terminal events, such as death and hospitalization, are frequently used as primary endpoints in cardiovascular clinical trials. The Win Ratio method (WR) employs a hierarchical structure to combine fatal and non-fatal events by giving death information an absolute priority, which can adversely affect power if the treatment effect is mainly on the non-fatal outcomes. We hereby propose the Win Ratio with Multiple Thresholds (WR-MT) that releases the strict hierarchical structure of the standard WR by adding stages with non-zero thresholds. A weighted adaptive approach is also developed to determine the thresholds in WR-MT. This method preserves the statistical properties of the standard WR but can sometimes increase the chance to detect treatment effects on non-fatal events. We show that WR-MT has a particularly favorable performance than standard WR when the second layer has stronger signals and otherwise comparable performance in our simulations that vary the follow-up time, the correlation between events, and the treatment effect sizes. A case study based on the Digitalis Investigation Group clinical trial data is presented to further illustrate our proposed method. An R package "WRMT" that implements the proposed methodology has been developed.