A0841
Title: Latent Gaussian process joint model for integrative analysis of multimodal biomarkers and initiation of medication
Authors: Kai Kang - Sun Yat-sen University (China) [presenting]
Abstract: Patients affected by Parkinson's disease (PD) usually experience a range of symptoms, including movement disorder, sleep disturbance, and brain structural changes, and may start treatment at certain time points throughout the clinical course. Current research focusing on a single modality fails to display the full picture of the disease progression and its connection with the timing of symptomatic therapy. The aim is to integrate longitudinal mixed types of measurements from multiple modalities and uncover their relationship with the initiation of PD treatment using a latent Gaussian process joint model. The dependence structure between observed multimodal biomarkers is characterized by several underlying unobserved Gaussian processes through a generalized factor model. Instead of assuming a certain parametric form a priori, the Gaussian processes, with its philosophy to let data speak for themselves, are expected to capture the possibly nonlinear and complex progression profiles of PD-related measurements. The obtained Gaussian processes are also incorporated into a varying coefficient Cox model so as to jointly monitor the longitudinal PD-related measurements and their time-varying effects on time-to-initiation of PD treatment. The application of the proposed method to the Parkinson's Progression Markers Initiative dataset yields a comprehensive understanding of PD progression by synthesizing information from clinical, biological, and neuroimaging perspectives.
