Estimating individualized treatment regimes from crossover designs.

TitleEstimating individualized treatment regimes from crossover designs.
Publication TypeJournal Article
Year of Publication2020
AuthorsNguyen, Crystal T., Daniel J. Luckett, Anna R. Kahkoska, Grace E. Shearrer, Donna Spruijt-Metz, Jaimie N. Davis, and Michael R. Kosorok
JournalBiometrics
Volume76
Issue3
Pagination778-788
Date Published2020 09
ISSN1541-0420
KeywordsCross-Over Studies, Humans, Learning, Machine Learning, Precision Medicine, Research Design
Abstract

The field of precision medicine aims to tailor treatment based on patient-specific factors in a reproducible way. To this end, estimating an optimal individualized treatment regime (ITR) that recommends treatment decisions based on patient characteristics to maximize the mean of a prespecified outcome is of particular interest. Several methods have been proposed for estimating an optimal ITR from clinical trial data in the parallel group setting where each subject is randomized to a single intervention. However, little work has been done in the area of estimating the optimal ITR from crossover study designs. Such designs naturally lend themselves to precision medicine since they allow for observing the response to multiple treatments for each patient. In this paper, we introduce a method for estimating the optimal ITR using data from a 2 × 2 crossover study with or without carryover effects. The proposed method is similar to policy search methods such as outcome weighted learning; however, we take advantage of the crossover design by using the difference in responses under each treatment as the observed reward. We establish Fisher and global consistency, present numerical experiments, and analyze data from a feeding trial to demonstrate the improved performance of the proposed method compared to standard methods for a parallel study design.

DOI10.1111/biom.13186
Alternate JournalBiometrics
Original PublicationEstimating individualized treatment regimes from crossover designs.
PubMed ID31743424
PubMed Central IDPMC7234899
Grant ListF30 DK113728 / DK / NIDDK NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
P60 MD002254 / MD / NIMHD NIH HHS / United States