Abstract

Identifying disease phenotypes based on longitudinal traits is a common goal in biomedical studies. Compared to clustering a single longitudinal trait, integrating multiple longitudinal traits allows additional information to be incorporated into the clustering process, which reveals co-existing longitudinal patterns and generates deeper biological insight. This talk will discuss a joint modeling approach for clustering multiple longitudinal traits with complex data structures. Results from analyzing real and simulated data will be presented and discussed.