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Preprints, Working Papers, ... Year : 2023

Random survival forests with multivariate longitudinal endogenous covariates


Predicting the individual risk of clinical events using the complete patient history is a major challenge in personalized medicine. Analytical methods have to account for a possibly large number of time-dependent predictors, which are often characterized by irregular and error-prone measurements, and are truncated early by the event. We extended the competing-risk random survival forests to handle such endogenous longitudinal predictors when predicting event probabilities. The method, implemented in the R package DynForest, internally transforms the time-dependent predictors at each node of each tree into time-fixed features (using mixed models) that can then be used as splitting candidates. The final individual event probability is computed as the average of leaf-specific Aalen-Johansen estimators over the trees. In an extensive simulation study, we showed that DynForest (i) was a relevant alternative to joint models for predicting an event with a limited number of longitudinal predictors, and (ii) outperformed the regression calibration techniques that ignore the informative truncation by the event when dealing with a large number of longitudinal predictors. Through an application in dementia research, we also illustrated how DynForest can be used to develop a dynamic prediction tool for dementia from multimodal repeated markers, and quantify the importance of each marker.
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Dates and versions

hal-03747106 , version 1 (08-08-2022)
hal-03747106 , version 2 (08-02-2023)
hal-03747106 , version 3 (21-09-2023)



Anthony Devaux, Catherine Helmer, Robin Genuer, Cécile Proust-Lima. Random survival forests with multivariate longitudinal endogenous covariates. 2023. ⟨hal-03747106v3⟩
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