  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.

Quantitative Methods;Machine Learning;Applications;Machine Learning