AN INTEGRATIVE MODELING FRAMEWORK FOR MULTIVARIATE LONGITUDINAL CLINICAL DATA

Abstract

This dissertation is centered on the development of innovative statistical methodologies specifically tailored to address the complex nature of multivariate data associated with Parkinson's disease (PD). PD is known to manifest impairments across both behavioral and cognitive domains, and as of now, there are no available disease-modifying treatments. Consequently, researchers typically gather a diverse array of data types, encompassing binary, continuous, categorical outcomes, and time-to-event data, in order to gain a comprehensive understanding of the multifaceted aspects of this disorder. Furthermore, due to the long follow-up of PD studies, the occurrence of intermittent missing data is a common challenge, which can introduce bias into the analysis. In this dissertation, we introduce novel approaches for jointly modeling multivariate longitudinal outcomes and time-to-event data using functional latent trait models and generalized multivariate functional mixed models to deal with different types of data. Additionally, we present a method for the detection and handling of missing data patterns through the utilization of joint modeling techniques.