Ensemble prediction of time‐to‐event outcomes with competing risks: a case‐study of surgical complications in Crohn's disease

We develop a novel algorithm to predict the occurrence of major abdominal surgery within 5 years following Crohn's disease diagnosis by using a panel of 29 baseline covariates from the Swedish population registers. We model pseudo‐observations based on the Aalen–Johansen estimator of the cause‐specific cumulative incidence with an ensemble of modern machine learning approaches. Pseudo‐observation preprocessing easily extends all existing or new machine learning procedures for continuous data to right‐censored event history data. We propose pseudo‐observation‐based estimators for the area under the time varying receiver operating characteristic curve, for optimizing the ensemble, and the predictiveness curve, for evaluating and summarizing predictive performance.