Abstract
Mediation analysis was concerned with the decomposition of the total effect of exposure on the outcome into the indirect effects and the remaining indirect effects, through a given mediation. However, when longitudinal data including time varying exposure and mediator variables, the estimated causal effects are affected by time varying confounders. Standard generalized linear equations did not give unbiased estimates. In this paper, we introduced inverse probability weighting technique to adjust such time varying confounders. Considering that the amount of data may be small and the distribution is not uniform, we decide to use Bayesian Inference to estimate the Structural Equation Model (SEM) parameters, and finally estimates the causal effect through counterfactual thought. This paper summarized the relevant theoretical knowledge of this method, verified the feasibility of this method by using the simulated data, and compared the performance of different methods.
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Acknowledgment
This work was supported by the key research and development project of Hubei Province “Research and Application of key Technologies of Intelligent Operation and maintenance and data security for 5G Micro data Center”, project number: 2020BAA001. We were grateful for the participation of all researchers and thanked project funding.
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Zhang, Y., Yang, L., Liu, F., Zhang, L., Zheng, J., Zhao, C. (2023). Bayesian Causal Mediation Analysis with Longitudinal Data. In: Qiu, M., Lu, Z., Zhang, C. (eds) Smart Computing and Communication. SmartCom 2022. Lecture Notes in Computer Science, vol 13828. Springer, Cham. https://doi.org/10.1007/978-3-031-28124-2_21
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DOI: https://doi.org/10.1007/978-3-031-28124-2_21
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