Time dependent ROC curves for the estimation of true prognostic capacity of microarray data.

Abstract

Microarray data can be used to identify prognostic signatures based on time-to-event data. The analysis of microarrays is often associated with overfitting and many papers have dealt with this issue. However, little attention has been paid to incomplete time-to-event data (truncated and censored follow-up). We have adapted the 0.632+ bootstrap estimator for the evaluation of time-dependent ROC curves. The interpretation of ROC-based results is well-established among the scientific and medical community. Moreover, the results do not depend on the incidence of the event, as opposed to many other prognostic statistics. Here, we have tested this methodology by simulations. We have illustrated its utility by analyzing a data set of diffuse large-B-cell lymphoma patients. Our results demonstrate the well-adapted properties of the 0.632+ ROC-based approach to evaluate the true prognostic capacity of a microarray-based signature. This method has been implemented in an R package ROCt632.