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Scalable and robust latent trajectory class analysis using artificial likelihood.

Kari R Hart1, Teng Fei2, John J Hanfelt2

  • 1The Peddie School, Hightstown, New Jersey.

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|September 8, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a faster, more practical method for analyzing population heterogeneity using artificial likelihoods. The new approach reliably identifies subgroups in complex datasets, especially those with non-Gaussian longitudinal data.

Keywords:
finite mixture modelgeneralized estimating equationlongitudinal dataprojected likelihoodquasi-likelihood

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Area of Science:

  • Biostatistics
  • Computational Biology
  • Data Science

Background:

  • Latent trajectory class analysis (LTCA) is crucial for understanding population heterogeneity.
  • Conventional LTCA methods struggle with large, non-Gaussian longitudinal datasets due to computational demands and strict modeling assumptions.

Purpose of the Study:

  • To develop a computationally tractable and flexible LTCA method.
  • To overcome the limitations of fully parametric models for non-Gaussian longitudinal data.
  • To enable robust subgroup identification in complex biological and medical research.

Main Methods:

  • Introduced a novel LTCA approach based on artificial likelihood concepts.
  • Avoided restrictive parametric modeling assumptions.
  • Evaluated computational efficiency and reliability compared to standard methods.

Main Results:

  • The new artificial likelihood-based method is significantly faster (20-200x) than conventional approaches for non-Gaussian data.
  • Demonstrated reliable estimation of population structure.
  • Successfully applied to identify subgroups in early neurodegeneration research participants.

Conclusions:

  • The artificial likelihood approach offers a computationally efficient and flexible alternative for LTCA.
  • This method enhances the ability to explore population heterogeneity in diverse research areas.
  • Facilitates subgroup discovery in complex longitudinal data, including neurodegenerative diseases.