Assessment of survival prediction models based on microarray data.
BACKGROUND
In the process of developing risk prediction models, various steps of model building and model selection are involved. If this process is not adequately controlled, overfitting may result in serious overoptimism leading to potentially erroneous conclusions.


METHODS
For right censored time-to-event data, we estimate the prediction error for assessing the performance of a risk prediction model (Gerds and Schumacher, 2006; Graf et al., 1999). Furthermore, resampling methods are used to detect overfitting and resulting overoptimism and to adjust the estimates of prediction error (Gerds and Schumacher, 2007).


RESULTS
We show how and to what extent the methodology can be used in situations characterized by a large number of potential predictor variables where overfitting may be expected to be overwhelming. This is illustrated by estimating the prediction error of some recently proposed techniques for fitting a multivariate Cox regression model applied to the data of a prognostic study in patients with diffuse large-B-cell lymphoma (DLBCL).


BACKGROUND
Resampling-based estimation of prediction error curves is implemented in an R package called pec available from the authors.
Schumacher
Martin
M
Department of Medical Biometry and Statistics, Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Germany. sec@imbi.uni-freiburg.de
Binder
Harald
H
Gerds
Thomas
T
D019295:Computational Biology / Q000379:methods*; D006801:Humans; D016013:Likelihood Functions; D016393:Lymphoma, B-Cell / Q000235:genetics* / Q000401:mortality*; D016403:Lymphoma, Large B-Cell, Diffuse / Q000235:genetics* / Q000401:mortality*; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011379:Prognosis; D016016:Proportional Hazards Models; D012044:Regression Analysis; D015203:Reproducibility of Results; D012306:Risk; D013997:Time Factors; D016896:Treatment Outcome