Maximin projection learning for optimal treatment decision with heterogeneous individualized treatment effects

Shi, Chengchun, Song, Rui, Lu, Wenbin and Fu, Bo (2018) Maximin projection learning for optimal treatment decision with heterogeneous individualized treatment effects. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 80 (4). 681 - 702. ISSN 1369-7412

Text (Maximin projection learning for optimal treatment decision with heterogeneous individualized treatment) - Accepted Version Available under License Creative Commons Attribution Non-commercial. Download (178kB)

• Scopus publication
• Author

Abstract

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.

Item Type:	Article
Official URL:	https://rss.onlinelibrary.wiley.com/journal/146798...
Additional Information:	© 2018 Royal Statistical Society
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	15 Oct 2019 12:30
Last Modified:	06 Apr 2024 20:54
URI:	http://eprints.lse.ac.uk/id/eprint/102112

Actions (login required)

View Item