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

Michael C. Sachs, Andrea Discacciati, Åsa H. Everhov, Ola Olén and Erin E. Gabriel

Journal of the Royal Statistical Society Series C, 2019, vol. 68, issue 5, 1431-1446

Abstract: 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.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1111/rssc.12367

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:68:y:2019:i:5:p:1431-1446

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().