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Digital technologies (e.g., mobile phones) can be used to obtain objective,frequent, and real-world digital phenotypes from individuals. However, modelingthese data poses substantial challenges since observational data are subject toconfounding and various sources of variabilities. For example, signals onpatients' underlying health status and treatment effects are mixed withvariation due to the living environment and measurement noises. The digitalphenotype data thus shows extensive variabilities between- and within-patientas well as across different health domains (e.g., motor, cognitive, andspeaking). Motivated by a mobile health study of Parkinson's disease (PD), wedevelop a mixed-response state-space (MRSS) model to jointly capturemulti-dimensional, multi-modal digital phenotypes and their measurementprocesses by a finite number of latent state time series. These latent statesreflect the dynamic health status and personalized time-varying treatmenteffects and can be used to adjust for informative measurements. Forcomputation, we use the Kalman filter for Gaussian phenotypes and importancesampling with Laplace approximation for non-Gaussian phenotypes. We conductcomprehensive simulation studies and demonstrate the advantage of MRSS inmodeling a mobile health study that remotely collects real-time digitalphenotypes from PD patients.

Mixed-Response State-Space Model for Analyzing Multi-Dimensional Digital Phenotypes