  Despite the availability of large amounts of genomics data, medical treatment
recommendations have not successfully used them. In this paper, we consider the
utility of high dimensional genomic-clinical data and nonparametric methods for
making cancer treatment recommendations. This builds upon the framework of the
individualized treatment rule [Qian and Murphy 2011] but we aim to overcome
their method's limitations, specifically in the instances when the method
encounters a large number of covariates and an issue of model misspecification.
We tackle this problem using a dimension reduction method, namely Sliced
Inverse Regression (SIR, [Li 1991]), with a rich class of models for the
treatment response. Notably, SIR defines a feature space for high-dimensional
data, offering an advantage similar to those found in the popular neural
network models. With the features obtained from SIR, a simple visualization is
used to compare different treatment options and present the recommended
treatment. Additionally, we derive the consistency and the convergence rate of
the proposed recommendation approach through a value function. The
effectiveness of the proposed approach is demonstrated through simulation
studies and the promising results from a real-data example of the treatment of
multiple myeloma.
