Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness.

TitleAscertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness.
Publication TypeJournal Article
Year of Publication2020
AuthorsDong, Lin, Eric Laber, Yair Goldberg, Rui Song, and Shu Yang
JournalStat Med
Volume39
Issue25
Pagination3503-3520
Date Published2020 11 10
ISSN1097-0258
KeywordsComputer Simulation, Humans, Models, Statistical, Precision Medicine, Probability
Abstract

Dynamic treatment regimes operationalize precision medicine as a sequence of decision rules, one per stage of clinical intervention, that map up-to-date patient information to a recommended intervention. An optimal treatment regime maximizes the mean utility when applied to the population of interest. Methods for estimating an optimal treatment regime assume the data to be fully observed, which rarely occurs in practice. A common approach is to first use multiple imputation and then pool the estimators across imputed datasets. However, this approach requires estimating the joint distribution of patient trajectories, which can be high-dimensional, especially when there are multiple stages of intervention. We examine the application of inverse probability weighted estimating equations as an alternative to multiple imputation in the context of monotonic missingness. This approach applies to a broad class of estimators of an optimal treatment regime including both Q-learning and a generalization of outcome weighted learning. We establish consistency under mild regularity conditions and demonstrate its advantages in finite samples using a series of simulation experiments and an application to a schizophrenia study.

DOI10.1002/sim.8678
Alternate JournalStat Med
Original PublicationAscertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness.
PubMed ID32729973
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01-DK-108073 / DK / NIDDK NIH HHS / United States