A Random Interaction Forest for Prioritizing Predictive Biomarkers

TL;DR

This paper introduces a new ensemble method called random interaction forest (RIF) designed to prioritize predictive biomarkers, demonstrating improved performance over traditional methods and providing meaningful insights in clinical trial data.

Contribution

The paper presents a novel tree-based ensemble approach, RIF, specifically developed for biomarker prioritization in precision medicine, filling a gap in existing statistical tools.

Findings

RIF outperforms random forest and univariable regression in simulations.

RIF identifies biologically meaningful biomarkers in clinical trial data.

The method provides reliable importance scores for biomarker selection.

Abstract

Precision medicine is becoming a focus in medical research recently, as its implementation brings values to all stakeholders in the healthcare system. Various statistical methodologies have been developed tackling problems in different aspects of this field, e.g., assessing treatment heterogeneity, identifying patient subgroups, or building treatment decision models. However, there is a lack of new tools devoted to selecting and prioritizing predictive biomarkers. We propose a novel tree-based ensemble method, random interaction forest (RIF), to generate predictive importance scores and prioritize candidate biomarkers for constructing refined treatment decision models. RIF was evaluated by comparing with the conventional random forest and univariable regression methods and showed favorable properties under various simulation scenarios. We applied the proposed RIF method to a biomarker dataset from two phase III clinical trials of bezlotoxumab on $\textit{Clostridium difficile}$ infection recurrence and obtained biologically meaningful results.