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Joint Models for Multiple Longitudinal Processes and Time-to-event Outcome.

Lili Yang1, Menggang Yu2, Sujuan Gao3

  • 1Biogen, 250 Binney Street, Cambridge, MA 02142.

Journal of Statistical Computation and Simulation
|December 7, 2016
PubMed
Summary

This study introduces a new maximum-likelihood method using the expectation-maximization (EM) algorithm for joint models. The method efficiently estimates associations between longitudinal data and time-to-event outcomes, like blood pressure and coronary artery disease.

Keywords:
EM algorithmjoint modelsmultiple longitudinal outcomessimulationtime-to-event outcome

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Longitudinal Data Analysis

Background:

  • Joint models statistically link time-to-event and longitudinal outcomes.
  • Computational complexity is a major challenge in applying joint models.
  • Existing methods include two-stage, Bayesian, and maximum-likelihood approaches.

Purpose of the Study:

  • To develop a computationally efficient maximum-likelihood estimation method for joint models with multiple longitudinal processes.
  • To assess the performance of the proposed expectation-maximization (EM) algorithm-based method.
  • To investigate the association between longitudinal blood pressure and time to coronary artery disease.

Main Methods:

  • Developed a maximum-likelihood estimation method for joint models using the expectation-maximization (EM) algorithm.
  • Considered joint models for a time-to-event outcome and multiple longitudinal processes.
  • Assessed method performance through simulations and application to real-world data.

Main Results:

  • The proposed EM algorithm-based maximum-likelihood method provides an efficient approach for joint modeling.
  • Simulations demonstrated the method's effectiveness in estimating associations.
  • The methodology was successfully applied to analyze blood pressure and coronary artery disease data.

Conclusions:

  • The developed EM algorithm-based maximum-likelihood method effectively addresses the computational challenges in joint modeling.
  • This approach enables robust estimation of associations between longitudinal measures and time-to-event outcomes.
  • The study highlights the utility of joint models in understanding cardiovascular disease risk factors.