  There is tremendous interest in precision medicine as a means to improve
patient outcomes by tailoring treatment to individual characteristics. An
individualized treatment rule formalizes precision medicine as a map from
patient information to a recommended treatment. A treatment rule is defined to
be optimal if it maximizes the mean of a scalar outcome in a population of
interest, e.g., symptom reduction. However, clinical and intervention
scientists often must balance multiple and possibly competing outcomes, e.g.,
symptom reduction and the risk of an adverse event. One approach to precision
medicine in this setting is to elicit a composite outcome which balances all
competing outcomes; unfortunately, eliciting a composite outcome directly from
patients is difficult without a high-quality instrument, and an expert-derived
composite outcome may not account for heterogeneity in patient preferences. We
propose a new paradigm for the study of precision medicine using observational
data that relies solely on the assumption that clinicians are approximately
(i.e., imperfectly) making decisions to maximize individual patient utility.
Estimated composite outcomes are subsequently used to construct an estimator of
an individualized treatment rule which maximizes the mean of patient-specific
composite outcomes. The estimated composite outcomes and estimated optimal
individualized treatment rule provide new insights into patient preference
heterogeneity, clinician behavior, and the value of precision medicine in a
given domain. We derive inference procedures for the proposed estimators under
mild conditions and demonstrate their finite sample performance through a suite
of simulation experiments and an illustrative application to data from a study
of bipolar depression.
