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A Latent-Class Model for Time-To-Event Outcomes and High-Dimensional Imaging Data.

Jiahui Feng1, Haolun Shi1, Ma Da2

  • 1Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada.

Statistics in Medicine
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Summary
This summary is machine-generated.

This study introduces a flexible latent-class model using structural MRI to identify Alzheimer's disease (AD) subtypes. The model reveals distinct patient groups for personalized AD treatment and research.

Keywords:
functional principal component analysisimage analysismixture modelssurvival analysistriangulation

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Area of Science:

  • Neuroimaging
  • Biostatistics
  • Computational Biology

Background:

  • Structural magnetic resonance imaging (MRI) is crucial for predicting Alzheimer's disease (AD) risk and enabling precision medicine.
  • Existing models often lack flexibility in capturing population heterogeneity and dynamic disease progression in AD research.

Purpose of the Study:

  • To propose a novel latent-class model for analyzing structural MRI data in Alzheimer's disease research.
  • To address population heterogeneity and model varying covariate-survival outcome relationships in AD progression.
  • To develop a robust computational framework for implementing the proposed statistical model.

Main Methods:

  • A latent-class model incorporating bivariate splines on triangulated brain image domains was developed.
  • A generalized expectation-maximization (EM) algorithm was designed, integrating logistic regression and penalized proportional hazards models.
  • The method was validated through extensive simulation studies and applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.

Main Results:

  • The proposed latent-class model effectively captures heterogeneity in Alzheimer's disease patient populations.
  • Bivariate splines accommodate the complex, irregular domains of brain imaging data.
  • The generalized EM algorithm provides an efficient computational approach for model implementation.

Conclusions:

  • The developed latent-class model offers a flexible and powerful tool for Alzheimer's disease research using structural MRI.
  • This approach facilitates the identification of distinct AD subtypes or disease stages, advancing precision medicine.
  • Application to the ADNI study demonstrates the model's utility in revealing disease heterogeneity.