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

Lu Cheng*, Siddharth Ramchandran, Tommi Vatanen, Niina Lietzén, Riitta Lahesmaa, Aki Vehtari, Harri Lähdesmäki

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)
174 Downloads (Pure)

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.

Original languageEnglish
Article number1798
Pages (from-to)1-11
Number of pages11
JournalNature Communications
Volume10
Issue number1
DOIs
Publication statusPublished - 17 Apr 2019
MoE publication typeA1 Journal article-refereed

Keywords

  • OUT CROSS-VALIDATION
  • INFERENCE

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