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


BACKGROUND
The proposed boosting approach is implemented in the R package CoxBoost and prediction error estimation in the package peperr, both available from CRAN.
Binder
Harald
H
Freiburg Center for Data Analysis and Modeling, University of Freiburg, Eckerstr 1 and Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Freiburg, Germany. binderh@fdm.uni-freiburg.de
Allignol
Arthur
A
Schumacher
Martin
M
Beyersmann
Jan
J
D000465:Algorithms; D020869:Gene Expression Profiling; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012044:Regression Analysis