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Activity Number: 358 - Contributed Poster Presentations: Biometrics Section
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #330207
Title: SuperLearning and Tree-Regression for Developing Treatment Rules That Optimize Health Outcomes
Author(s): Andre Kurepa Waschka*
Companies: University of California, Berkeley
Keywords: SuperLearner; Decision Trees; Tuning Parameter

The augmented decision tree-based method presented here is a procedure that uses cross-validated, variance-bias trade-off to develop treatment rules for optimizing health outcomes. This method chooses the most refined level of stratification in order to minimize misclassification rates by incorporating differential impacts for a false positive (FP) versus false negative (FN) errors. The new tree-based estimator is characterized by a tuning parameter ?, which is a loss matrix composed of user-supplied weights (FP; FN). Our optimized CV method directly optimizes the weighted FP to FN ratio while capitalizing on a SuperLearning based cross-validation to limit the risk of overfitting. This yields an estimator that minimizes the cross-validated risk estimates.

Clinical applications of this approach suggest this method has great promise as a statistical tool for precision medicine.

Authors who are presenting talks have a * after their name.

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