Semiparametric Partial Functional Regression Model for Estimating Optimal Individualized Treatment Regime

Estimating the optimal individualized treatment regimes based on patients' characteristic information has become an increasingly important topic in personalized medicine study. These characteristic data can range from simple scalar values to complex functional data such as curves or images, which might be taken into account to recommend more beneficial treatment decisions for patients. In this paper, we propose a novel semiparametric partial functional regression model for estimating the optimal individualized treatment regimes with scalar and functional covariates. One advantage of this model is that it involves a fully nonparametric main effect of the covariates and a flexible interaction effect between the covariates and the treatment, and greatly reduces the risk of model misspecification. The form of single index interaction effect with a monotone link function ensures the estimated optimal individualized treatment regime preserving good interpretability. We estimate this model via B-spline and establish the convergence rate of the estimated optimal individualized treatment regime. Sufficient simulation studies and a real data analysis are conducted to assess the performance of the proposed method.