Random Forests for time-fixed and time-dependent predictors: The DynForest R package

Anthony Devaux (1) , Cécile Proust-Lima (1) , Robin Genuer (1)

1 BPH - Bordeaux population health

The R package DynForest implements random forests for predicting a categorical or a (multiple causes) time-to-event outcome based on time-fixed and time-dependent predictors. Through the random forests, the time-dependent predictors can be measured with error at subject-specific times, and they can be endogeneous (i.e., impacted by the outcome process). They are modeled internally using flexible linear mixed models (thanks to lcmm package) with time-associations pre-specified by the user. DynForest computes dynamic predictions that take into account all the information from time-fixed and time-dependent predictors. DynForest also provides information about the most predictive variables using variable importance and minimal depth. Variable importance can also be computed on groups of variables. To display the results, several functions are available such as summary and plot functions. This paper aims to guide the user with a step-by-step example of the different functions for fitting random forests within DynForest.

Keywords: dynamic prediction; survival data; competing risks; regression; classification; longitudinal predictors; random forests; R

Domains: Machine Learning [stat.ML]

Anthony Devaux, Cécile Proust-Lima, Robin Genuer. Random Forests for time-fixed and time-dependent predictors: The DynForest R package. 2023. ⟨hal-03970683v1⟩