The sequential treatment decisions made by physicians to treat chronic diseases
are formalized in the statistical literature as dynamic treatment regimes. To
date, methods for dynamic treatment regimes have been developed under the
assumption that observation times, that is, treatment and outcome monitoring
times, are determined by study investigators. That assumption is often not
satisfied in electronic health records data in which the outcome, the
observation times, and the treatment mechanism are associated with patients’
characteristics. The treatment and observation processes can lead to spurious
associations between the treatment of interest and the outcome to be optimized
under the dynamic treatment regime if not adequately considered in the analysis.
We address these associations by incorporating two inverse weights that are
functions of a patient’s covariates into dynamic weighted ordinary least squares
to develop optimal single stage dynamic treatment regimes, known as
individualized treatment rules. We show empirically that our methodology yields
consistent, multiply robust estimators. In a cohort of new users of
antidepressant drugs from the United Kingdom’s Clinical Practice Research
Datalink, the proposed method is used to develop an optimal treatment rule that
chooses between two antidepressants to optimize a utility function related to
the change in body mass index.