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.

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