Causal inference with marginal structural modeling for longitudinal data in laparoscopic surgery: A technical note

查看全文 作　　者：Zhongheng [1,2,3]Zhang;Peng [4]Jin;Menglin [5]Feng;Jie [1]Yang;Jiajie [1]Huang;Lin [6]Chen;Ping [7,8,9]Xu;Jian [10]Sun;Caibao [11]Hu;Yucai [1]Hong 机构地区：[1]Department of Emergency Medicine,Sir Run Run Shaw Hospital,Zhejiang University School of Medicine,Hangzhou 310016,China;[2]Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province,Hangzhou 310016,China;[3]Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province,Hangzhou 310016,China;[4]Division of Biostatistics,Department of Population Health,New York University School of Medicine,New York,NY 10023,USA;[5]Saw Swee Hock School of Public Health and Institute of Data Science,National University of Singapore,119077,Singapore;[6]Department of Critical Care Medicine,Jinhua Hospital Affiliated Zhejiang University School of Medicine,Jinhua 321000,China;[7]Emergency Department,Zigong Fourth People's Hospital,Zigong 643000,China;[8]Institute of Medical Big Data,Zigong Academy of Artificial Intelligence and Big Data for Medical Science Artificial Intelligence,Zigong 643000,China;[9]Key Laboratory of Sichuan Province,Zigong 643000,China;[10]Department of Critical Care Medicine,Lishui Center Hospital,Lishui 323000,China;[11]Department of Intensive Care Medicine,Zhejiang Hospital,Hangzhou 310013,China 出　　处：《Laparoscopic, Endoscopic and Robotic Surgery》索引2022年第5卷第4期,共7页 基　　金：funding from the National Natural Science Foundation of China(82272180);Open Foundation of Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province(SZZD202206);funding from the Sichuan Medical Association Scientific Research Project(S21019);funding from the Key Research and Development Project of Zhejiang Province(2021C03071);funding from Zhejiang Medical and Health Science and Technology Project(2017ZD001)。 摘　　要：Causal inference prevails in the field of laparoscopic surgery.Once the causality between an intervention and outcome is established,the intervention can be applied to a target population to improve clinical outcomes.In many clinical scenarios,interventions are applied longitudinally in response to patients’conditions.Such longitudinal data comprise static variables,such as age,gender,and comorbidities;and dynamic variables,such as the treatment regime,laboratory variables,and vital signs.Some dynamic variables can act as both the confounder and mediator for the effect of an intervention on the outcome;in such cases,simple adjustment with a conventional regression model will bias the effect sizes.To address this,numerous statistical methods are being developed for causal inference;these include,but are not limited to,the structural marginal Cox regression model,dynamic treatment regime,and Cox regression model with time-varying covariates.This technical note provides a gentle introduction to such models and illustrates their use with an example in the field of laparoscopic surgery. 关 键 词：Causal inference Laparoscopic surgery Machine learning Marginal structural modeling