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Related Concept Videos

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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Related Experiment Video

Updated: Dec 12, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Random forests for high-dimensional longitudinal data.

Louis Capitaine1, Robin Genuer1, Rodolphe Thiébaut1

  • 1INSERM U1219 Bordeaux Population Health Research Center, INRIA Bordeaux Sud-Ouest, SISTM Team, Bordeaux University, Bordeaux, France.

Statistical Methods in Medical Research
|August 11, 2020
PubMed
Summary

This study introduces a novel random forest method for analyzing high-dimensional longitudinal data, improving insights from repeated measurements. The approach effectively identifies key gene transcripts associated with HIV viral load in patient data.

Keywords:
Stochastic mixed effects modelhigh-dimensional datarepeated measurementstree-based methods

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Area of Science:

  • Statistics
  • Bioinformatics
  • Machine Learning

Background:

  • Random forests are effective for high-dimensional data (p >> n).
  • Repeated measurements in longitudinal data offer valuable information, especially in high-dimensional contexts.
  • Existing tree-based methods adapt to clustered/longitudinal data using mixed-effects models.

Purpose of the Study:

  • To propose a general random forest approach for high-dimensional longitudinal data.
  • To develop a method incorporating intra-individual covariance for random forest construction.
  • To evaluate estimation methods for high-dimensional longitudinal data.

Main Methods:

  • Developed a flexible stochastic model for time-varying covariance structures.
  • Introduced a new random forest method accounting for intra-individual covariance.
  • Conducted simulation experiments to assess estimation methods.

Main Results:

  • The proposed random forest method performs well in high-dimensional longitudinal settings.
  • Simulation studies demonstrated the behavior of different estimation techniques.
  • Applied the method to HIV vaccine trial data.

Conclusions:

  • The novel random forest approach effectively handles high-dimensional longitudinal data.
  • The method identified 21 relevant gene transcripts associated with HIV viral load.
  • Findings are consistent with primary HIV infection observations.