Work

Random Forests for Time-Fixed and Time-Dependent Predictors: The DynForest R Package

Year: 2025

Type: article

Abstract: The R package DynForest implements random forests for predicting a continuous, a categorical, or a (multiple causes) time-to-event outcome based on time-fixed and time-dependent predictors. The main originality of DynForest is that it handles time-dependent predictors that can be endogenous (i.e., impacted by the outcome process), measured with error, and measured at subject-specific times. At each recursive step of the tree building process, the time-dependent predictors are internally summarized into individual features on which the split can be done. This is achieved using flexible linear mixed models (thanks to the R package lcmm), whose specification is pre-specified by the user. DynForest returns the mean for a continuous outcome, the category with a majority vote for a categorical outcome, or the cumulative incidence function over time for a survival outcome. DynForest also computes variable importance and minimal depth to inform on the most predictive variables or groups of variables. This paper aims to guide the user with step-by-step examples for fitting random forests using DynForest.

Source: The R Journal

DOI: 10.32614/rj-2025-002

Authors: Anthony Devaux, Cécile Proust-Lima, Robin Genuer

Institutions: Université de Bordeaux, Inserm, UNSW Sydney, Bordeaux Population Health, The George Institute for Global Health

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

Funder: Agence Nationale de la Recherche

Awards: G4656594441, Dynamic Models for Epidemiological Longitudinal Studies of Chronic Diseases