Predictive Directions for Individualized Treatment Selection in Clinical Trials

TL;DR

This paper introduces predictive directions, a novel approach combining sufficient dimension reduction and causal inference to identify individualized treatment effects in clinical trials, including nonlinear extensions with support vector machines.

Contribution

It develops a new framework for estimating personalized treatment effects using predictive directions, integrating SDR and causal inference, with extensions to nonlinear models.

Findings

Effective in colorectal cancer trials

Handles both linear and nonlinear treatment effects

Provides a new tool for personalized medicine

Abstract

In many clinical trials, individuals in different subgroups have experience differential treatment effects. This leads to individualized differences in treatment benefit. In this article, we introduce the general concept of predictive directions, which are risk scores motivated by potential outcomes considerations. These techniques borrow heavily from sufficient dimension reduction (SDR) and causal inference methodology. Under some conditions, one can use existing methods from the SDR literature to estimate the directions assuming an idealized complete data structure, which subsequently yields an obvious extension to clinical trial datasets. In addition, we generalize the direction idea to a nonlinear setting that exploits support vector machines. The methodology is illustrated with application to a series of colorectal cancer clinical trials.