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Neural ODE enhanced linear mixed effect models for estimating complex association patterns of time-varying covariates with the marker trajectory
Zhe Aurore Li, Quentin Clairon, Cécilia Samieri +3
Longitudinal cohort studies produce repeated data that enable the assessment of time-varying association patterns between exposures and health outcomes. Classical linear mixed-effe…
Random Forests for time-fixed and time-dependent predictors: The DynForest R package
Anthony Devaux, Cécile Proust-Lima, Robin Genuer
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 pre…
Random survival forests with multivariate longitudinal endogenous covariates
Anthony Devaux, Catherine Helmer, Robin Genuer +1
Predicting the individual risk of a clinical event using the complete patient history is still a major challenge for personalized medicine. Among the methods developed to compute i…
Individual dynamic prediction of clinical endpoint from large dimensional longitudinal biomarker history: a landmark approach
Anthony Devaux, Robin Genuer, Karine Pérès +1
The individual data collected throughout patient follow-up constitute crucial information for assessing the risk of a clinical event, and eventually for adapting a therapeutic stra…