Ensemble prediction of time‐to‐event outcomes with competing risks: a case‐study of surgical complications in Crohn's disease. Issue 5 (18th July 2019)

• Record Type: Journal Article
• Authors: Sachs, Michael C.; Discacciati, Andrea; Everhov, Åsa H.; Olén, Ola; Gabriel, Erin E.
• Is Part Of: Journal of the Royal Statistical Society. Volume 68:Issue 5(2019)
• Page Start: 1431
• Page End: 1446
• Publication Date: 2019-07-18
• Subjects: Competing risks -- Crohn's disease -- Inflammatory bowel disease -- Machine learning -- Pseudo‐observations; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12367
• Languages: English
• ISSNs: 0035-9254

Abstract

Summary: 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.