DEVAUX, Anthony; HELMER, Catherine; GENUER, Robin; PROUST LIMA, Cecile

doi:10.1177/09622802231206477

Bordeaux population health [BPH]

HELMER, Catherine

Bordeaux population health [BPH]

GENUER, Robin

Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]

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Article de revue

Ce document a été publié dans

Statistical Methods in Medical Research. 2023-12-01, vol. 32, n° 12, p. 2331-2346

Résumé en anglais

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. In this work, 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. Using simulations, we compared the performances of DynForest to accurately predict an event with (i) a joint modeling alternative when considering two longitudinal predictors only, and with (ii) a regression calibration method that ignores 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.< Réduire

Mots clés en anglais

Individual dynamic prediction

Multivariate predictors

Random survival forest

Longitudinal data

Survival data

Competing risks

URI

https://oskar-bordeaux.fr/handle/20.500.12278/186605

DOI

http://dx.doi.org/10.1177/09622802231206477

Unités de recherche

Bordeaux Population Health Research Center (BPH) - UMR 1219

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Random survival forests with multivariate longitudinal endogenous covariates

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Résumé en anglais

Mots clés en anglais

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