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Activity Number: 625
Type: Invited
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract #318182 View Presentation
Title: Personalized Treatment for Longitudinal Data Using Unspecified Random-Effects Model
Author(s): Hyunkuen Cho and Peng Wang and Annie Qu*
Companies: Western Michigan University and University of Cincinnati and University of Illinois at Urbana-Champaign
Keywords: Quadratic inference functions ; penalized quasi-likelihood ; personalized treatment ; generalized linear mixed model ; random forest

We develop new modeling for personalized treatment for longitudinal studies involving high heterogeneity of treatment effects. Incorporating subject-specific information into the treatment assignment is crucial since different individuals could react to the same treatment very differently. We estimate unobserved subject-specific treatment effects through conditional random-effects modeling, and apply the random forest algorithm to allocate effective treatments for individuals. The advantage of our approach is that random-effects estimation does not rely on the normality assumption. In theory, we show that the proposed random-effect estimator is consistent and more efficient than the random-effect estimator which ignores correlation information from longitudinal data. Simulation studies and a data example from an HIV clinical trial also confirm that the proposed method can efficiently identify the best treatments for individual patients.

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

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