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September 2021 A deep learning semiparametric regression for adjusting complex confounding structures
Xinlei Mi, Patrick Tighe, Fei Zou, Baiming Zou
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Ann. Appl. Stat. 15(3): 1086-1100 (September 2021). DOI: 10.1214/21-AOAS1481

Abstract

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.

Acknowledgments

We thank the Editor and anonymous reviewers for their insightful comments and constructive suggestions, which have greatly improved the paper.

Citation

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Xinlei Mi. Patrick Tighe. Fei Zou. Baiming Zou. "A deep learning semiparametric regression for adjusting complex confounding structures." Ann. Appl. Stat. 15 (3) 1086 - 1100, September 2021. https://doi.org/10.1214/21-AOAS1481

Information

Received: 1 June 2019; Revised: 1 March 2020; Published: September 2021
First available in Project Euclid: 23 September 2021

Digital Object Identifier: 10.1214/21-AOAS1481

Keywords: Bootstrap aggregating , comparative effectiveness analysis , complex confounding , deep neural network , propensity score , semiparametric regression

Rights: Copyright © 2021 Institute of Mathematical Statistics

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Vol.15 • No. 3 • September 2021
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