Online Program Home
My Program

Abstract Details

Activity Number: 612
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract #319697
Title: Estimating Individualized Treatment Rules for Ordinal Treatments
Author(s): Jingxiang Chen* and Yufeng Liu and Michael R. Kosorok and Haoda Fu and Xuanyao He
Companies: and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and Eli Lilly and Company and Eli Lilly and Company
Keywords: Data Duplication ; Individual Treatment Rule ; Optimal Individual Dose Finding ; Ordinal Treatment ; Outcome Weighted Learning
Abstract:

Precision medicine is an emerging scientific area for disease treatment and prevention by considering individual variability. In clinical trial studies, one of the main goals is to obtain an optimal individual treatment rule (ITR) which can help treatment selection according to each patient's attributes. Recently, outcome weighted learning (OWL) was proposed to estimate such an optimal ITR in the two-arm setting by maximizing the expected clinical outcome. However, it is unclear how OWL would work for the ordinal treatment settings such as dose finding. Furthermore, OWL requires data transformation when the outcome has negative values. In this paper, we propose a new method to estimate ITR with ordinal treatments. In particular, we use a data duplication technique with a piecewise convex loss function. We establish Fisher consistency for the estimated ITR and convergence properties for the risk bound. Numerical examples show the highly competitive performance of the new method.


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

Back to the full JSM 2016 program

 
 
Copyright © American Statistical Association