Estimating individualized treatment rules for ordinal treatments.

TitleEstimating individualized treatment rules for ordinal treatments.
Publication TypeJournal Article
Year of Publication2018
AuthorsChen, Jingxiang, Haoda Fu, Xuanyao He, Michael R. Kosorok, and Yufeng Liu
JournalBiometrics
Volume74
Issue3
Pagination924-933
Date Published2018 09
ISSN1541-0420
KeywordsDecision Support Techniques, Diabetes Mellitus, Type 2, Humans, Models, Statistical, Observational Studies as Topic, Precision Medicine, Treatment Outcome
Abstract

Precision medicine is an emerging scientific topic for disease treatment and prevention that takes into account individual patient characteristics. It is an important direction for clinical research, and many statistical methods have been proposed recently. One of the primary goals of precision medicine is to obtain an optimal individual treatment rule (ITR), which can help make decisions on treatment selection according to each patient's specific characteristics. Recently, outcome weighted learning (OWL) has been proposed to estimate such an optimal ITR in a binary treatment setting by maximizing the expected clinical outcome. However, for ordinal treatment settings, such as individualized dose finding, it is unclear how to use OWL. In this article, we propose a new technique for estimating ITR with ordinal treatments. In particular, we propose a data duplication technique with a piecewise convex loss function. We establish Fisher consistency for the resulting estimated ITR under certain conditions, and obtain the convergence and risk bound properties. Simulated examples and an application to a dataset from a type 2 diabetes mellitus observational study demonstrate the highly competitive performance of the proposed method compared to existing alternatives.

DOI10.1111/biom.12865
Alternate JournalBiometrics
Original PublicationEstimating individualized treatment rules for ordinal treatments.
PubMed ID29534296
PubMed Central IDPMC6136994
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 GM126550 / GM / NIGMS NIH HHS / United States