BST 290 Seminar Series
Tuesday, April 21st, 2015, 4:10pm, MSB 1147 (Colloquium Room)
Refreshments at 3:30pm in MSB 4110 (Statistics Lounge)
Speaker: Annette Molinaro, Associate Professor, Department of Neurological Surgery and Division of Epidemiology and Biostatistics, UC San Francisco
Title: Tree derived Survival risk groups in differentiating care for glioma patients
Abstract: 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. As the resulting models continue to take the form of a decision tree, partDSA also provides an ideal foundation for developing a clinician-friendly tool for accurate risk prediction and stratification.
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. In this talk, various useful extensions of partDSA for right-censored outcomes are described and we show the power of the partDSA algorithm in deriving survival risk groups for glioma patient based on genomic markers.