Piecewise Constant Estimation for Prediction of Survival Outcomes: Applications in Genomics

• Activity Number: 303
• Type: Contributed
• Date/Time: Wednesday, August 11, 2004 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Computing
• Author(s): Annette Molinaro*+ and Mark van der Laan
• Companies: University of California, Berkeley and University of California, Berkeley
• Keywords: prediction ; survival analysis ; model selection ; regression trees ; loss function ; comparative genomic hybridization

Abstract

Clinicians and researchers collect a tremendous amount of data on cancer patients in the hopes of finding significant prognostic factors. Medical studies commonly involve thousands of clinical, epidemiological, and genomic measurements collected on each patient, along with a time to the clinical event of interest, such as disease recurrence or death. Over the past several decades there have been numerous attempts to use nonparametric methods with this type of data to find an estimator of outcome. A common approach is to modify classification and regression trees (CART), specifically for right-censored data. This presentation includes a generalization of CART based on a unified strategy for estimator construction, selection, and performance assessment in the presence of censoring. In this approach, the parameter of interest is defined as the risk minimizer for a suitable loss function and candidate estimators are generated with CART using this loss function. Cross-validation is applied to select an optimal estimator among the candidates and to assess the overall performance of the resulting estimator.