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Time Varying Mixed Effects Model with Fused Lasso Regularization.

Jaehong Yu1,2, Hua Zhong1,2

  • 1Department of Industrial and Management Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Republic of Korea.

Journal of Applied Statistics
|July 9, 2021
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Summary

This study introduces a new model to analyze how health conditions affect aging, revealing dynamic changes over time. The fused lasso-based time-varying linear mixed effect (FTLME) model captures these evolving associations for better health insights.

Keywords:
Fused lassoLinear mixed effect modelLongitudinal analysisRegularizationTime-varying fixed effect

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Gerontology

Background:

  • Covariate associations with outcomes can change over time, impacting health trajectories.
  • Traditional models may miss these dynamic effects, especially for aging populations.
  • Understanding age-varying impacts of chronic diseases on physical function is crucial.

Purpose of the Study:

  • To propose a novel statistical model for estimating time-varying covariate effects.
  • To develop an efficient algorithm for parameter estimation in longitudinal studies.
  • To analyze the age-varying impact of chronic diseases on physical function in older adults.

Main Methods:

  • Developed a fused lasso-based time-varying linear mixed effect (FTLME) model.
  • Implemented an efficient two-stage parameter estimation algorithm.
  • Utilized simulation studies and real-world data from the Health and Retirement Study (HRS).

Main Results:

  • The FTLME model effectively estimates longitudinal trajectories of fixed-effect coefficients.
  • The proposed algorithm demonstrates computational efficiency in high-dimensional settings.
  • Simulation studies confirm the method's efficacy in capturing smooth time-varying effects.

Conclusions:

  • The FTLME model provides a robust approach to analyzing time-varying covariate effects in aging research.
  • This method enhances the understanding of how chronic conditions dynamically influence older adults' physical functions.
  • The approach is practical for real-world health data analysis, offering valuable insights into aging processes.