Joint Model for Survival and Multivariate Sparse Functional Data with Application to a Study of Alzheimer's Disease

TL;DR

This paper introduces a multivariate functional joint model for analyzing correlated longitudinal outcomes and their association with Alzheimer's disease onset, providing interpretable insights into disease progression.

Contribution

It proposes a novel multivariate functional mixed model that captures shared and outcome-specific progression patterns in sparse functional data related to AD.

Findings

Model successfully applied to ADNI data revealing associations between outcomes and AD onset.

Simulation studies confirm the model's validity and interpretability.

Provides new insights into the progression patterns of Alzheimer's disease.

Abstract

Studies of Alzheimer's disease (AD) often collect multiple longitudinal clinical outcomes, which are correlated and predictive of AD progression. It is of great scientific interest to investigate the association between the outcomes and time to AD onset. We model the multiple longitudinal outcomes as multivariate sparse functional data and propose a functional joint model linking multivariate functional data to event time data. In particular, we propose a multivariate functional mixed model (MFMM) to identify the shared progression pattern and outcome-specific progression patterns of the outcomes, which enables more interpretable modeling of associations between outcomes and AD onset. The proposed method is applied to the Alzheimer's Disease Neuroimaging Initiative study (ADNI) and the functional joint model sheds new light on inference of five longitudinal outcomes and their associations with AD onset. Simulation studies also confirm the validity of the proposed model.