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A Functional Joint Model for Survival and Multivariate Sparse Functional Data in Multi-Cohort Alzheimer's Disease

Wenyi Wang1, Luo Xiao1, Ruonan Li1

  • 1Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA.

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This study introduces a new statistical model for Alzheimer's disease (AD) research, integrating multiple data types to track disease progression and survival across diverse patient groups.

Keywords:
EM algorithmfunctional datamultivariate longitudinal datapenalized splines

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Area of Science:

  • Biostatistics
  • Neurodegenerative Diseases
  • Longitudinal Data Analysis

Background:

  • Alzheimer's disease (AD) research often involves complex, multi-study data with missing outcomes.
  • Existing models may not adequately integrate longitudinal health data with survival information, limiting comprehensive analysis.

Purpose of the Study:

  • To develop an integrative joint model for analyzing multivariate sparse functional and survival data in Alzheimer's disease (AD) across multiple studies.
  • To extend the multivariate functional mixed model (MFMM) to handle missing-by-design outcomes in multi-cohort studies.

Main Methods:

  • Developed an extended multivariate functional mixed model (MFMM) integrating longitudinal outcomes and time-to-event data.
  • Employed a parsimonious survival model to link disease progression trajectories to survival outcomes.
  • Utilized penalized splines within an Expectation-Maximization (EM) algorithm for efficient parameter estimation.

Main Results:

  • The model successfully captured shared disease progression trajectories and accounted for inter-cohort variability in Alzheimer's disease.
  • Application to three AD cohorts demonstrated the model's ability to integrate diverse data types effectively.
  • Simulation studies confirmed the robustness and accuracy of the proposed statistical framework.

Conclusions:

  • The integrative joint model provides a flexible and interpretable framework for analyzing complex Alzheimer's disease data across multiple studies.
  • This approach enhances the understanding of AD progression and supports clinical decision-making in multi-cohort research settings.
  • The model's ability to handle sparse functional and survival data makes it valuable for future neurodegenerative disease research.