Policy Learning for Optimal Individualized Dose Intervals
This work addresses the need for personalized medical dosing intervals in healthcare, representing a novel extension beyond existing dose recommendation methods.
The authors tackled the problem of learning individualized dose intervals from observational data, proposing a new method called probability dose interval (PDI) that guarantees outcomes above a threshold with a given probability, and demonstrated its advantage over benchmarks in simulations and a diabetes case study.
We study the problem of learning individualized dose intervals using observational data. There are very few previous works for policy learning with continuous treatment, and all of them focused on recommending an optimal dose rather than an optimal dose interval. In this paper, we propose a new method to estimate such an optimal dose interval, named probability dose interval (PDI). The potential outcomes for doses in the PDI are guaranteed better than a pre-specified threshold with a given probability (e.g., 50%). The associated nonconvex optimization problem can be efficiently solved by the Difference-of-Convex functions (DC) algorithm. We prove that our estimated policy is consistent, and its risk converges to that of the best-in-class policy at a root-n rate. Numerical simulations show the advantage of the proposed method over outcome modeling based benchmarks. We further demonstrate the performance of our method in determining individualized Hemoglobin A1c (HbA1c) control intervals for elderly patients with diabetes.