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Abstract Details

Activity Number: 471
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract - #305901
Title: Random Forest with Regression in the Nodes
Author(s): Georgiy Bobashev*+ and Barry Eggleston and Nikhil Garge
Companies: RTI International and RTI International and RTI International
Address: 3040 Cornwallis Rd, Durham, NC, 27709-2194, United States
Keywords: personalized treatment ; comparative effectiveness ; random forest ; predictive modeling ; regression ; weak predictor

The goal of personalized treatment is to identify treatment with the highest expected success given individual factors. Although random forests provide stable predictions, they only produce simple data summaries (e.g. prevalence) in the nodes and thus don't allow consideration of "what if" scenarios. Recently we have developed methodology (mobForest package available through CRAN contributed repository) that combines random forests with regressions in the nodes. Thus, for an individual with a specific combination of factors our approach provides smooth and stable prediction of potential treatment outcomes under several alternative treatments. The models are validated using independent training/test datasets. We illustrate the use of the method on a dataset from the largest clinical trial of several alcohol treatment approaches called COMBINE. We show that while one of the treatments is best for most of the subjects there are individuals who could benefit from alternative treatments.

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