Near-optimal Individualized Treatment Recommendations
This work addresses the need for more flexible treatment recommendations in precision medicine, though it is incremental as it builds on existing outcome weighted learning frameworks.
The authors tackled the problem of providing flexible treatment recommendations in precision medicine by developing methods for alternative individualized treatment recommendations (A-ITR), which offer multiple near-optimal options, and demonstrated their usefulness through simulation studies and application to Type 2 diabetic patient data.
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.