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Activity Number: 394 - Challenges and New Directions in Precision Medicine for Large-Scale and Complex Data
Type: Invited
Date/Time: Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
Sponsor: Biometrics Section
Abstract #309282
Title: On Restricted Optimal Treatment Regime Estimation for Competing Risks Data
Author(s): Jie Zhou and Jiajia Zhang and Wenbin Lu * and Xiaoming Li
Companies: University of South Carolina and University of South Carolina and North Carolina State University and University of South Carolina
Keywords: Competing Risks Data; Cumulative Incidence Function; Optimal Treatment Regime; Side Effects; Value Search Method

It is well accepted that individualized treatment regimes may improve the clinical outcomes of interest. However, positive treatment effects are often accompanied by certain side effects. Therefore, when choosing the optimal treatment regime for a patient, we need to consider both efficacy and safety issues. In this work, we propose to model time to a primary event of interest and time to severe side effects of treatment by a competing risks model and define a restricted optimal treatment regime based on cumulative incidence functions. The estimation approach is derived using a penalized value search method and investigated through extensive simulations. The proposed method is applied to an HIV dataset obtained from Health Sciences South Carolina, where we minimize the risk of treatment or virologic failures while controlling the risk of serious drug-induced side effects.

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

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