Bayesian Joint Modeling with Global-Local Selection for Advancing Precision Medicine

Music City Center 

Neovascular Age-related Macular Degeneration (wet AMD) is a chronic eye disorder. While it accounts for only about 10-15% of all AMD cases, it is responsible for the majority of severe vision impairment associated with AMD, making early detection and management crucial. In the context of precision medicine for wet AMD, we introduce a novel joint model of longitudinal Drusen biomarkers and AMD progression. The proposed model is designed to handle a large set of longitudinal biomarkers by incorporating a nested structure that accounts for subject-biomarker interactions. The model employs nonparametric functional trajectories using a 'nested Dirichlet process prior' to manage nested clustering where subject clusters are nested within biomarker clusters. The proposed method's nested structure supports a more comprehensive and scalable approach, enabling the model to capture the heterogeneous effects of biomarkers across different patient subgroups. For instance, variable selection is performed both globally (to identify key biomarkers) and locally (to examine their varying effects across subgroups). This allows the model to highlight situations where a biomarker may have opposite effects on different subgroups, offering valuable insights to medical experts on patient stratification. We evaluate the performance of the proposed approach through simulation studies and apply it to real-world data analysis. Our findings align with recent AMD literature while also identifying drusen biomarkers previously deemed insignificant, exhibiting both positive and negative effects on AMD progression, varying across different patient subgroups.