Quantifying the predictive accuracy of time-to-event models in the presence of competing risks

DOI10.1002/BIMJ.201000073zbMath1207.62209OpenAlexW2011446631WikidataQ39790649 ScholiaQ39790649MaRDI QIDQ3084185

Harald Binder, Jan Beyersmann, Rotraut Schoop, Martin Schumacher

Publication date: 15 March 2011

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/bimj.201000073

zbMATH Keywords

prediction errorquadratic lossBrier scoreIPCW

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Censored data models (62N01) Medical applications (general) (92C50) Estimation in survival analysis and censored data (62N02)

Related Items (7)

Penalized variable selection in competing risks regression ⋮ Multiple imputation methods for inference on cumulative incidence with missing cause of failure ⋮ Explained variation for recurrent event data ⋮ Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time‐to‐event in presence of censoring and competing risks ⋮ Discussion of “A Risk-Based Measure of Time-Varying Prognostic Discrimination for Survival Models,” by C. Jason Liang and Patrick J. Heagerty ⋮ Measures of prediction error for survival data with longitudinal covariates ⋮ Robust prediction of the cumulative incidence function under non‐proportional subdistribution hazards

Uses Software

• cmprsk

Cites Work

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