Random survival forests with multivariate longitudinal endogenous covariates
| dc.rights.license | open | en_US |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | DEVAUX, Anthony | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | HELMER, Catherine | |
| hal.structure.identifier | Statistics In System biology and Translational Medicine [SISTM] | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | GENUER, Robin | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | PROUST LIMA, Cecile
ORCID: 0000-0002-9884-955X IDREF: 114375747 | |
| dc.date.accessioned | 2023-12-13T15:21:46Z | |
| dc.date.available | 2023-12-13T15:21:46Z | |
| dc.date.issued | 2023-12-01 | |
| dc.identifier.issn | 1477-0334 | en_US |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/186605 | |
| dc.description.abstractEn | 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. | |
| dc.language.iso | EN | en_US |
| dc.subject.en | Individual dynamic prediction | |
| dc.subject.en | Multivariate predictors | |
| dc.subject.en | Random survival forest | |
| dc.subject.en | Longitudinal data | |
| dc.subject.en | Survival data | |
| dc.subject.en | Competing risks | |
| dc.title.en | Random survival forests with multivariate longitudinal endogenous covariates | |
| dc.title.alternative | Stat Methods Med Res | en_US |
| dc.type | Article de revue | en_US |
| dc.identifier.doi | 10.1177/09622802231206477 | en_US |
| dc.subject.hal | Sciences du Vivant [q-bio]/Santé publique et épidémiologie | en_US |
| dc.identifier.pubmed | 37886845 | en_US |
| bordeaux.journal | Statistical Methods in Medical Research | en_US |
| bordeaux.page | 2331-2346 | en_US |
| bordeaux.volume | 32 | en_US |
| bordeaux.hal.laboratories | Bordeaux Population Health Research Center (BPH) - UMR 1219 | en_US |
| bordeaux.issue | 12 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | INSERM | en_US |
| bordeaux.institution | INRIA | en_US |
| bordeaux.team | LEHA_BPH | en_US |
| bordeaux.team | SISTM_BPH | en_US |
| bordeaux.team | BIOSTAT__BPH | en_US |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.inpress | non | en_US |
| hal.popular | non | en_US |
| hal.audience | Internationale | en_US |
| hal.export | false | |
| dc.rights.cc | Pas de Licence CC | en_US |
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