Assessing Prediction Error in Survival Times: Application to Ovarian Cancer

Random survival forests (RSFs) are a tool for predicting survival times. Forests are grown using a set of (possibly censored) survival times with associated predictor variables. One challenge surrounding the use of RSFs is the assessment of prediction error; mean squared error (MSE), the commonly used measure of prediction error in the traditional random forest setting, is not an option if some observations are censored. An alternative measure of prediction error that has been suggested in the literature is the C-index, which summarizes the concordance between the observed and predicted values and can incorporate some censored cases. However, in this talk, we show that this measure of prediction error can behave in undesirable ways (and quite differently than MSE). We then present some alternative ways of thinking about prediction error in the context of predicting recurrence time of ovarian cancer.