Online Program Home
My Program

Abstract Details

Activity Number: 72 - Semiparametric Modeling
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract #305057 Presentation
Title: Double Deep Learning for Adjusting Complex Confounding Structures
Author(s): Xinlei Mi*
Companies: Columbia University
Keywords: Bootstrap aggregating; comparative effectiveness analysis; complex confounding; deep neural network; semiparametric regression

Complex confounding structures are often embedded in electronic medical record (EMR) data. A robust yet efficient double deep learning approach is proposed to adjust the complex confounding structures in comparative effectiveness analysis of EMR data. Specifically, deep neural networks are employed to estimate the conditional expectation of both the outcome and the treatment allocation given observed baseline covariates under the semiparametric framework (Robinson, 1988). An improved estimation scheme is further developed to enhance the performance under finite sample scenarios. Comprehensive numerical studies have shown the superior performance of the proposed methods, as compared with other existing methods, with remarkably reduced bias and mean squared error in parameter estimates. An application to a post-surgery pain study is also conducted by using the proposed methods and other competing methods. Finally, an R package, Deep Treatment Learning “deepTL”, is developed to implement the proposed method.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program