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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References
Qiu, M., Jia, Z., et al.: Voltage assignment with guaranteed probability satisfying timing constraint for real-time multiprocessor DSP. J. Signal Proc. Systems (2007)
Qiu, M., Yang, L., et al.: Dynamic and leakage energy minimization with soft real-time loop scheduling and voltage assignment. IEEE Trans. VLSI 18(3), 501–504 (2009)
Qiu, M., Li, H., Sha, E.: Heterogeneous real-time embedded software optimization considering hardware platform. ACM Symposium on Applied Computing, pp. 1637–1641 (2009)
Niu, J., Gao, Y., et al.: Selecting proper wireless network interfaces for user experience enhancement with guaranteed probability. JPDC 72(12), 1565–1575 (2012)
Qiu, M., Chen, Z., Ming, Z., Qin, X., Niu, J.: Energy-aware data allocation with hybrid memory for mobile cloud systems. IEEE Syst. J. 11(2), 813–822 (2014)
Qiu, M., Xue, C., Shao, Z., et al.: Efficient algorithm of energy minimization for heterogeneous wireless sensor network. IEEE EUC, pp. 25–34 (2006)
Gai, K., Qiu, M., Chen, L., Liu, M.: Electronic health record error prevention approach using ontology in big data. In: IEEE 17th HPCC (2015)
Gai, K., Qiu, M., Elnagdy, S.: A novel secure big data cyber incident analytics framework for cloud-based cybersecurity insurance. IEEE BigDataSecurity (2016)
Zhang, K., Kong, J., et al.: Multimedia layout adaptation through grammatical specifications. Multimedia Syst. 10(3), 245–260 (2005)
Qiu, M., Xue, C., Shao, Z., Sha, E.: Energy minimization with soft real-time and DVS for uniprocessor and multiprocessor embedded systems. IEEE DATE Conference, pp. 1–6 (2007)
Li, J., Ming, Z., et al.: Resource allocation robustness in multi-core embedded systems with inaccurate information. J. Syst. Architect. 57(9), 840–849 (2011)
Gai, K., Zhang, Y., et al.: Blockchain-enabled service optimizations in supply chain digital twin. IEEE Trans. Serv. Comput. (2022)
Li, Y., Gai, K., et al.: Intercrossed access controls for secure financial services on multimedia big data in cloud systems. ACM Trans. MCCA (2016)
Qiu, M., Qiu, H., et al.: Secure Data Sharing Through Untrusted Clouds with Blockchain-enabled Key Management. The 3rd SmartBlock, pp. 11–16, China (2020)
Qiu, H., Zheng, Q., et al.: Topological graph convolutional network-based urban traffic flow and density prediction. IEEE Trans. ITS (2020)
Qiu, M., Qiu, H.: Review on image processing based adversarial example defenses in computer vision. In: IEEE 6th International Conference BigDataSecurity, pp. 94–99, USA (2020)
Qiu, H., Dong, T., et al.: Adversarial attacks against network intrusion detection in IoT systems. IEEE Internet Things J. 8(13), 10327–10335 (2020)
Gao, X., Qiu, M.: Energy-Based Learning for Preventing Backdoor Attack. KSEM , vol. 3, pp. 706–721 (2022)
Zhang, L., Qiu, M., Tseng, W., Sha, E.: Variable partitioning and scheduling for MPSoC with virtually shared scratch pad memory. J. Sign. Process. Syst. 58(2), 247–265 (2010)
Hu, F., Lakdawala, S., et al.: Low-power, intelligent sensor hardware interface for medical data preprocessing. IEEE Trans. Infor. Tech. Biomed. 13(4), 656–663 (2009)
Caleb, H.M., et al.: On partial identification of the pure direct effect. arXiv preprint arXiv:1509.01652 (2015)
Tyler, J.V.W.: A unification of mediation and interaction: a four-way decomposition. Epidemiology, vol. 25.5, p. 749. Cambridge, Mass (2014)
Judea, P.: Direct and indirect effects. Probabilistic and Causal Inference: The Works of Judea Pearl, pp. 373–392 (2022)
Mittinty, M.N., Vansteelandt, S.: Longitudinal mediation analysis using natural effect models. Am. J. Epidemiol. 189(11), 1427–1435 (2020)
Clare, P.J., Dobbins, T.A., Mattick, R.P.: Causal models adjusting for time-varying confounding—a systematic review of the literature. Int. J. Epidemiol. 48(1), 254–265 (2019)
Baldwin, S.A., Larson, M.J.: An introduction to using Bayesian linear regression with clinical data. Behav. Res. Ther. 98, 58–75 (2017)
Fan, Y., et al.: Applications of structural equation modeling (SEM) in ecological studies: an updated review. Ecol. Process. 5(1), 1–12 (2016). https://doi.org/10.1186/s13717-016-0063-3
Huang, J., Yuan, Y.: Bayesian dynamic mediation analysis. Psychol. Methods 22(4), 667 (2017)
Robins, J.M., Hernan, M.A., Brumback, B.: Marginal structural models and causal inference in epidemiology. Epidemiology 11(5), 550–560 (2000)
Tyler, J.V.W., Tchetgen, E.J.: Mediation analysis with time varying exposures and mediators. J. Royal Statist. Soc. Ser. B (Statistical Methodology) 79.3, 917–938 (2017)
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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