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Activity Number:
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195
- Topics in Personalized/Precision Medicine - II
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Type:
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Contributed
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Date/Time:
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Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract #310977
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Title:
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Boosting Algorithms for Estimating Optimal Individualized Treatment Rules
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Author(s):
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Duzhe Wang* and Haoda Fu and Po-Ling Loh
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Companies:
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University of Wisconsin-Madison and Eli Lilly and Company and UW-Madison
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Keywords:
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Additive trees;
Boosting algorithms ;
Direct learning ;
Individualized treatment rules;
XGBoost
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Abstract:
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We present nonparametric algorithms for estimating optimal individualized treatment rules. The proposed algorithms are based on the XGBoost algorithm, which is known as one of the most powerful algorithms in the machine learning literature. Our main idea is to model the conditional mean of clinical outcome or the decision rule via additive regression trees, and use the boosting technique to estimate each single tree iteratively. Our approaches overcome the challenge of correct model specification which is required in current parametric methods. The major contribution of our proposed algorithms is providing the efficient and accurate estimation of the highly nonlinear and complex optimal individualized treatment rules which arise in practice. Finally, we illustrate the superior performance of our algorithms by extensive simulation studies and conclude with an application to the real data from a diabetes Phase III trial. This work received JSM Biopharmaceutical Section Student Paper Award.
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Authors who are presenting talks have a * after their name.
