Data-guided Treatment Recommendation with Feature Scores

TL;DR

This paper introduces a novel treatment recommendation method using dimension reduction via Sliced Inverse Regression to handle high-dimensional genomic data, improving upon existing individualized treatment rules.

Contribution

It proposes a new approach combining SIR with nonparametric models for better treatment recommendations in high-dimensional settings, addressing limitations of previous methods.

Findings

Effective in simulation studies

Promising results on multiple myeloma data

Provides theoretical guarantees for consistency

Abstract

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.