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Title: Personalized treatment selection via the covariate-specific treatment effect curve for longitudinal data
Award ID(s):
2014221
NSF-PAR ID:
10329944
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Statistical Theory and Related Fields
Volume:
5
Issue:
3
ISSN:
2475-4269
Page Range / eLocation ID:
253 to 264
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  1. Summary

    A salient feature of data from clinical trials and medical studies is inhomogeneity. Patients not only differ in baseline characteristics, but also in the way that they respond to treatment. Optimal individualized treatment regimes are developed to select effective treatments based on patient's heterogeneity. However, the optimal treatment regime might also vary for patients across different subgroups. We mainly consider patients’ heterogeneity caused by groupwise individualized treatment effects assuming the same marginal treatment effects for all groups. We propose a new maximin projection learning method for estimating a single treatment decision rule that works reliably for a group of future patients from a possibly new subpopulation. Based on estimated optimal treatment regimes for all subgroups, the proposed maximin treatment regime is obtained by solving a quadratically constrained linear programming problem, which can be efficiently computed by interior point methods. Consistency and asymptotic normality of the estimator are established. Numerical examples show the reliability of the methodology proposed.

     
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