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Activity Number: 553 - Improving Patient Care Through Personalized/Stratified Medicine: Modern Data Science and Perspectives from Pharmaceutical Industry Leads and Regulatory
Type: Invited
Date/Time: Thursday, August 6, 2020 : 3:00 PM to 4:50 PM
Sponsor: Biopharmaceutical Section
Abstract #309403
Title: Pruned Targeted-Learning for Personalized Medicine
Author(s): Yixin Fang*
Companies: AbbVie Inc.
Keywords: Causal inference ; Estimand; Optimal estimator; Targeted learning

In personalized medicine, optimal individualized treatment rules can be estimated via either outcome regression estimators or inverse probability weighted estimators. The strengths of both estimating methods can be combined using the targeted learning, leading to targeted maximum likelihood estimators (TMLE). To simplify estimated treatment rules, we can apply the decision-tree procedure to prune them and make them more interpretable in practice. In addition, we can use the data-splitting strategy to unbiasedly estimate the values of the pruned treatment rules. The performance of the proposed approach is demonstrated via simulations and a real application.

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

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