Estimation of average treatment effect based on a multi-index propensity score

BMC Medical Research Methodology

Research

Kecheng Wei¹  Yaxin Xue¹  Ce Wang¹  Jiaqin Xu¹  Chen Huang¹  Rui Zhang¹  Yongfu Yu²  Guoyou Qin² 

[1] Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China;Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China;Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China;Shanghai Institute of Infectious Disease and Biosecurity, Shanghai, China;

关键词: Average treatment effect;    Multiply robust;    Multi-index propensity score;    Artificial neural network;   

DOI  :  10.1186/s12874-022-01822-3

 received in 2022-08-25, accepted in 2022-12-16,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundEstimating the average effect of a treatment, exposure, or intervention on health outcomes is a primary aim of many medical studies. However, unbalanced covariates between groups can lead to confounding bias when using observational data to estimate the average treatment effect (ATE). In this study, we proposed an estimator to correct confounding bias and provide multiple protection for estimation consistency.MethodsWith reference to the kernel function-based double-index propensity score (Ker.DiPS) estimator, we proposed the artificial neural network-based multi-index propensity score (ANN.MiPS) estimator. The ANN.MiPS estimator employed the artificial neural network to estimate the MiPS that combines the information from multiple candidate models for propensity score and outcome regression. A Monte Carlo simulation study was designed to evaluate the performance of the proposed ANN.MiPS estimator. Furthermore, we applied our estimator to real data to discuss its practicability.ResultsThe simulation study showed the bias of the ANN.MiPS estimators is very small and the standard error is similar if any one of the candidate models is correctly specified under all evaluated sample sizes, treatment rates, and covariate types. Compared to the kernel function-based estimator, the ANN.MiPS estimator usually yields smaller standard error when the correct model is incorporated in the estimator. The empirical study indicated the point estimation for ATE and its bootstrap standard error of the ANN.MiPS estimator is stable under different model specifications.ConclusionsThe proposed estimator extended the combination of information from two models to multiple models and achieved multiply robust estimation for ATE. Extra efficiency was gained by our estimator compared to the kernel-based estimator. The proposed estimator provided a novel approach for estimating the causal effects in observational studies.

【 授权许可】

CC BY   
© The Author(s) 2022

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