A Random Interaction Forest for Prioritizing Predictive Biomarkers

Autor:	Zeng, Zhen, Lu, Yuefeng, Shen, Judong, Zheng, Wei, Shaw, Peter, Dorr, Mary Beth
Rok vydání:	2019
Předmět:	Quantitative Biology - Quantitative Methods Computer Science - Machine Learning Statistics - Applications Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 15 pages, 2 figures, 2 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1910.01786 Zobrazit plný text záznamu View this record from Arxiv