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**A deep learning semiparametric regression for adjusting complex confounding structures.**
*(English)*
Zbl 1478.62284

Summary: 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.

### MSC:

62M45 | Neural nets and related approaches to inference from stochastic processes |

62G08 | Nonparametric regression and quantile regression |

62G09 | Nonparametric statistical resampling methods |

62P10 | Applications of statistics to biology and medical sciences; meta analysis |

68T07 | Artificial neural networks and deep learning |

### Keywords:

bootstrap aggregating; comparative effectiveness analysis; complex confounding; deep neural network; propensity score; semiparametric regression
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\textit{X. Mi} et al., Ann. Appl. Stat. 15, No. 3, 1086--1100 (2021; Zbl 1478.62284)

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