Propensity score analysis with missing data using a multi-task neural network.

Journal: BMC medical research methodology
PMID: 36793016

Abstract

BACKGROUND: Propensity score analysis is increasingly used to control for confounding factors in observational studies. Unfortunately, unavoidable missing values make estimating propensity scores extremely challenging. We propose a new method for estimating propensity scores in data with missing values.

Authors

  • Shu Yang
    Department of Health Management, Bengbu Medical College, Bengbu, 233030.
  • Peipei Du
    West China Biomedical Big Data Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China.
  • Xixi Feng
    Department of Public Health, Chengdu Medical College, Sichuan, China.
  • Daihai He
    Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China.
  • Yaolong Chen
    Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
  • Linda L D Zhong
    Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, People's Republic of China.
  • Xiaodong Yan
    Ming Hsieh Department of Electrical Engineering , University of Southern California , Los Angeles , California 90089 , United States.
  • Jiawei Luo

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