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.