Bagging and deep learning in optimal individualized treatment rules. Issue 2 (29th March 2019)

• Record Type: Journal Article
• Authors: Mi, Xinlei; Zou, Fei; Zhu, Ruoqing
• Is Part Of: Biometrics. Volume 75:Issue 2(2019)
• Page Start: 674
• Page End: 684
• Publication Date: 2019-03-29
• Subjects: Bootstrap aggregating -- deep neural network -- high‐dimensional data -- outcome weighted learning -- personalized medicine; Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.12990
• Languages: English
• ISSNs: 0006-341X

Abstract

Abstract: An ENsemble Deep Learning Optimal Treatment (EndLot) approach is proposed for personalized medicine problems. The statistical framework of the proposed method is based on the outcome weighted learning (OWL) framework which transforms the optimal decision rule problem into a weighted classification problem. We further employ an ensemble of deep neural networks (DNNs) to learn the optimal decision rule. Utilizing the flexibility of DNNs and the stability of bootstrap aggregation, the proposed method achieves a considerable improvement over existing methods. An R package "ITRlearn" is developed to implement the proposed method. Numerical performance is demonstrated via simulation studies and a real data analysis of the Cancer Cell Line Encyclopedia data.