Kernel Assisted Learning for Personalized Dose Finding
Aug 13, 20203 views
An individualized dose rule recommends a dose level within a continuous safe dose range based on patient level information such,as physical conditions, genetic factors and medication histories.,Traditionally, personalized dose finding process requires repeating,clinical visits of the patient and frequent adjustments of the dosage.,Thus the patient is constantly exposed to the risk of underdosing,and overdosing during the process. Statistical methods for finding,an optimal individualized dose rule can lower the costs and risks,for patients. In this article, we propose a kernel assisted learning,method for estimating the optimal individualized dose rule. The,proposed methodology can also be applied to all other continuous,decision-making problems. Advantages of the proposed method,include robustness to model misspecification and capability of providing statistical inference for the estimated parameters. In the,simulation studies, we show that this method is capable of identifying the optimal individualized dose rule and produces favorable,expected outcomes in the population. Finally, we illustrate our approach using data from a warfarin dosing study for thrombosis,patients.