  To promote precision medicine, individualized treatment regimes (ITRs) are
crucial for optimizing the expected clinical outcome based on patient-specific
characteristics. However, existing ITR research has primarily focused on
scenarios with categorical treatment options and a single outcome. In reality,
clinicians often encounter scenarios with continuous treatment options and
multiple, potentially competing outcomes, such as medicine efficacy and
unavoidable toxicity. To balance these outcomes, a proper weight is necessary,
which should be learned in a data-driven manner that considers both patient
preference and clinician expertise. In this paper, we present a novel algorithm
for developing individualized treatment regimes (ITRs) that incorporate
continuous treatment options and multiple outcomes, utilizing observational
data. Our approach assumes that clinicians are optimizing individualized
patient utilities with sub-optimal treatment decisions that are at least better
than random assignment. Treatment assignment is assumed to directly depend on
the true underlying utility of the treatment rather than patient
characteristics. The proposed method simultaneously estimates the weighting of
composite outcomes and the decision-making process, allowing for construction
of individualized treatment regimes with continuous doses. The proposed
estimators can be used for inference and variable selection, facilitating the
identification of informative treatment assignments and preference-associated
variables. We evaluate the finite sample performance of our proposed method via
simulation studies and apply it to a real data application of radiation
oncology analysis.
