JSM 2005 - Toronto

Abstract #304273

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 94
Type: Topic Contributed
Date/Time: Monday, August 8, 2005 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #304273
Title: Crossvalidated and Bagged Partitioning Estimators with Variable Importance
Author(s): Annette Molinaro*+
Companies: NCI/Yale University
Address: 6120 Executive Blvd MSC 7244, Rockville, MD, 20852, United States
Keywords: variable importance ; piecewise constant estimation ; prediction ; high-dimensional data ; genomics ; bagging
Abstract:

Clinicians aim toward a more preventive model of attacking cancer by pinpointing and targeting specific early events in disease development. These early events can be measured as genomic, proteomic, epidemiologic, and clinical variables. Such measurements are then used to predict clinical outcomes such as primary occurrence or mortality. Recursive partitioning algorithms seek to explain the individual contributions of various covariates as well as their interactions for the purposes of predicting outcomes. There are several important considerations when using such algorithms. The first is to not overfit the data; the second is the stability of the resulting predictor. For example, CART is sensitive to data fluctuations and, given a perturbation, will potentially build a different predictor than that built on the original data. A third consideration is variable importance. We present crossvalidated and bagged estimators using both CART and DSA-Part algorithms for a proteomic dataset. We explore the benefits of the suggested bagging scheme and introduce a data adaptive ranking for assessing each covariate's contribution.

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Revised March 2005