title: Boosting for high-dimensional time-to-event data with competing risks.
abstract: 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.
authors.affiliation: 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
authors.forename: Harald
authors.initials: H
authors.lastname: Binder
authors.forename: Arthur
authors.initials: A
authors.lastname: Allignol
authors.forename: Martin
authors.initials: M
authors.lastname: Schumacher
authors.forename: Jan
authors.initials: J
authors.lastname: Beyersmann
mesh_terms: 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