Title | Estimating individualized treatment rules for ordinal treatments. |
Publication Type | Journal Article |
Year of Publication | 2018 |
Authors | Chen, Jingxiang, Haoda Fu, Xuanyao He, Michael R. Kosorok, and Yufeng Liu |
Journal | Biometrics |
Volume | 74 |
Issue | 3 |
Pagination | 924-933 |
Date Published | 2018 Sep |
ISSN | 1541-0420 |
Keywords | Decision 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. |
DOI | 10.1111/biom.12865 |
Alternate Journal | Biometrics |
Original Publication | Estimating individualized treatment rules for ordinal treatments. |
PubMed ID | 29534296 |
PubMed Central ID | PMC6136994 |
Grant List | P01 CA142538 / CA / NCI NIH HHS / United States R01 GM126550 / GM / NIGMS NIH HHS / United States |