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An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data

Author

Listed:
  • Lu Cheng

    (Aalto University School of Science
    Cardiff University)

  • Siddharth Ramchandran

    (Aalto University School of Science)

  • Tommi Vatanen

    (Broad Institute of MIT and Harvard
    University of Auckland)

  • Niina Lietzén

    (University of Turku and Åbo Akademi University)

  • Riitta Lahesmaa

    (University of Turku and Åbo Akademi University)

  • Aki Vehtari

    (Aalto University School of Science)

  • Harri Lähdesmäki

    (Aalto University School of Science)

Abstract

Biomedical research typically involves longitudinal study designs where samples from individuals are measured repeatedly over time and the goal is to identify risk factors (covariates) that are associated with an outcome value. General linear mixed effect models are the standard workhorse for statistical analysis of longitudinal data. However, analysis of longitudinal data can be complicated for reasons such as difficulties in modelling correlated outcome values, functional (time-varying) covariates, nonlinear and non-stationary effects, and model inference. We present LonGP, an additive Gaussian process regression model that is specifically designed for statistical analysis of longitudinal data, which solves these commonly faced challenges. LonGP can model time-varying random effects and non-stationary signals, incorporate multiple kernel learning, and provide interpretable results for the effects of individual covariates and their interactions. We demonstrate LonGP’s performance and accuracy by analysing various simulated and real longitudinal -omics datasets.

Suggested Citation

  • Lu Cheng & Siddharth Ramchandran & Tommi Vatanen & Niina Lietzén & Riitta Lahesmaa & Aki Vehtari & Harri Lähdesmäki, 2019. "An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09785-8
    DOI: 10.1038/s41467-019-09785-8
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    Cited by:

    1. Monterrubio-Gómez, Karla & Roininen, Lassi & Wade, Sara & Damoulas, Theodoros & Girolami, Mark, 2020. "Posterior inference for sparse hierarchical non-stationary models," Computational Statistics & Data Analysis, Elsevier, vol. 148(C).
    2. Tommi Välikangas & Tomi Suomi & Courtney E. Chandler & Alison J. Scott & Bao Q. Tran & Robert K. Ernst & David R. Goodlett & Laura L. Elo, 2022. "Benchmarking tools for detecting longitudinal differential expression in proteomics data allows establishing a robust reproducibility optimization regression approach," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    3. Chunzheng Cao & Ming He & Jian Qing Shi & Xin Liu, 2021. "Estimation and prediction of a generalized mixed-effects model with t-process for longitudinal correlated binary data," Computational Statistics, Springer, vol. 36(2), pages 1461-1479, June.

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