Bootstrap estimated true and false positive rates and ROC curve
Computational Statistics & Data Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Gene Selection Using Iterative Feature Elimination Random Forests for Survival Outcomes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
| Hi-index | 3.84 |
Motivation: 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). Availability: Resampling-based estimation of prediction error curves is implemented in an R package called pec available from the authors. Contact: sec@imbi.uni-freiburg.de