Journal Article

Boosting for high-dimensional time-to-event data with competing risks

Harald Binder, Arthur Allignol, Martin Schumacher and Jan Beyersmann

in Bioinformatics

Volume 25, issue 7, pages 890-896
Published in print April 2009 | ISSN: 1367-4803
Published online February 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp088
Boosting for high-dimensional time-to-event data with competing risks

More Like This

Show all results sharing this subject:

  • Bioinformatics and Computational Biology

GO

Show Summary Details

Preview

Motivation: For analyzing high-dimensional time-to-event data with competing risks, tailored modeling techniques are required that consider the event of interest and the competing events at the same time, while also dealing with censoring. For low-dimensional settings, proportional hazards models for the subdistribution hazard have been proposed, but an adaptation for high-dimensional settings is missing. In addition, tools for judging the prediction performance of fitted models have to be provided.

Results: We propose a boosting approach for fitting proportional subdistribution hazards models for high-dimensional data, that can e.g. incorporate a large number of microarray features, while also taking clinical covariates into account. Prediction performance is evaluated using bootstrap.632+ estimates of prediction error curves, adapted for the competing risks setting. This is illustrated with bladder cancer microarray data, where simultaneous consideration of both, the event of interest and competing events, allows for judging the additional predictive power gained from incorporating microarray measurements.

Availability: The proposed boosting approach is implemented in the R package CoxBoost and prediction error estimation in the package peperr, both available from CRAN.

Contact: binderh@fdm.uni-freiburg.de

Journal Article.  5686 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

Full text: subscription required

How to subscribe Recommend to my Librarian

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.