DEEP LEARNING SEMIPARAMETRIC REGRESSION FOR ADJUSTING COMPLEX CONFOUNDING STRUCTURES

• 成果类型: Article
• 署名作者: Mi, Xinlei; Tighe, Patrick; Zou, Fei; Zou, Baiming
• 署名单位: Northwestern University; State University System of Florida; University of Florida; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1481
• 发表日期: 2021
• 页码: 1086-1100
• 关键词: electronic health records; propensity score; postoperative pain; neural-network; adjustment; surgery; trials

摘要

Deep Treatment Learning (deepTL), a robust yet efficient deep learning-based semiparametric regression approach, is proposed to adjust the complex confounding structures in comparative effectiveness analysis of observational data, for example, electronic health record (EHR) data in which complex confounding structures are often embedded. Specifically, we develop a deep learning neural network with a score-based ensembling scheme for flexible function approximation. An improved semiparametric procedure is further developed to enhance the performance of the proposed method under finite sample settings. Comprehensive numerical studies have demonstrated the superior performance of the proposed methods, as compared with existing methods, with a remarkably reduced bias and mean squared error in parameter estimates. The proposed research is motivated by a postsurgery pain study, which is also used to illustrate the practical application of deepTL. Finally, an R package, deepTL, is developed to implement the proposed method.