BACKGROUND: We recently developed partDSA, a multivariate method that, similarly to CART, utilizes loss functions to select and partition predictor variables to build a tree-like regression model for a given outcome. However, unlike CART, partDSA permits both 'and' and 'or' conjunctions of predictors, elucidating interactions between variables as well as their independent contributions. partDSA thus permits tremendous flexibility in the construction of predictive models and has been shown to supersede CART in both prediction accuracy and stability. METHODS: With right-censored outcomes, partDSA currently builds estimators via either the Inverse Probability Censoring Weighted (IPCW) or Brier Score weighting schemes; see Lostritto, Strawderman and Molinaro (2012), where it is shown in numerous simulations that both proposed adaptations for partDSA perform as well, and often considerably better, than two competing tree-based methods. RESULTS: We show the power of the partDSA algorithm in deriving survival risk groups for glioma patient based on genomic markers. CONCLUSIONS: As the resulting models take the form of a decision tree, partDSA provides an ideal foundation for developing a clinician-friendly tool for accurate risk prediction and stratification. SECONDARY CATEGORY: Epidemiology & Cancer Control.