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Activity Number: 468 - Statistical Methods in Clinical Trials
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract #307228
Title: A Flexible Bayesian Method to Individualized Treatment Allocation
Author(s): Saptarshi Chatterjee* and Sanjib Basu
Companies: Northern Illinois University and University of Illinois at Chicago
Keywords: Personalized Medicine; Optimal treatment assignment; Observational Study; Bayesian Method; Semi-parametric method
Abstract:

There is an increasing interest of making informed treatment decisions depending on the patients' biological characteristics. The optimal treatment choice is a function which takes the patients' information and returns the best treatment option as an output. A number of methods have been proposed in the recent precision medicine literature where they employ semi-parametric estimation methods such as inverse probability weighting (IPWE) to predict the optimal treatment by maximizing a certain predefined value function. However, the likelihood-based methods have received little attention in this area. To fill the gap, we propose a semi-parametric Bayesian method to predict the optimal treatment for a new patient based on the treatment and covariate information from an observed group of patients. We illustrated the performance of the proposed method and compared with the existing methods proposed in literature.


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

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