  Individualized treatment recommendation (ITR) is an important analytic
framework for precision medicine. The goal is to assign proper treatments to
patients based on their individual characteristics. From the machine learning
perspective, the solution to an ITR problem can be formulated as a weighted
classification problem to maximize the average benefit that patients receive
from the recommended treatments. Several methods have been proposed for ITR in
both binary and multicategory treatment setups. In practice, one may prefer a
more flexible recommendation with multiple treatment options. This motivates us
to develop methods to obtain a set of near-optimal individualized treatment
recommendations alternative to each other, called alternative individualized
treatment recommendations (A-ITR). We propose two methods to estimate the
optimal A-ITR within the outcome weighted learning (OWL) framework. We show the
consistency of these methods and obtain an upper bound for the risk between the
theoretically optimal recommendation and the estimated one. We also conduct
simulation studies, and apply our methods to a real data set for Type 2
diabetic patients with injectable antidiabetic treatments. These numerical
studies have shown the usefulness of the proposed A-ITR framework. We develop a
R package aitr which can be found at https://github.com/menghaomiao/aitr.
