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Cross-Modal Multivariate Pattern Analysis
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Composite likelihood for joint analysis of multiple multistate processes via copulas.

Liqun Diao1, Richard J Cook2

  • 1Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, 14642, United States.

Biostatistics (Oxford, England)
|April 11, 2014
PubMed
Summary

This study introduces a novel copula-based model for analyzing multiple progressive multistate processes, offering flexibility in specifying marginal models and facilitating various estimation methods for complex health data.

Keywords:
Composite likelihoodCopula modelInterval censoringMarkov processMultiplicative intensityMultistate model

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Epidemiology

Background:

  • Joint modeling of multiple progressive multistate processes is crucial for understanding complex disease trajectories.
  • Existing intensity-based or frailty-based methods have limitations in retaining marginal process features.

Purpose of the Study:

  • To propose a flexible copula-based model for joint analysis of multiple progressive multistate processes.
  • To ensure the joint model retains the features of specified marginal multistate processes.
  • To facilitate various estimation and inference approaches, including composite likelihood and two-stage procedures.

Main Methods:

  • Developed a copula formulation for joint modeling of multistate processes.
  • Focused on processes with Markov margins, suitable for progressive chronic diseases.
  • Addressed intermittent examinations and interval-censored transition times.

Main Results:

  • The copula model allows for a wide range of marginal multistate processes to be specified.
  • The formulation supports composite likelihood and two-stage estimation.
  • Simulation studies provided empirical insights into the analysis methods.

Conclusions:

  • The proposed copula-based model offers a flexible and robust framework for joint analysis of progressive multistate processes.
  • This approach is particularly useful for longitudinal health data with intermittent observations.
  • The model was illustrated with an application to psoriatic arthritis progression.