Guidance for a causal comparative effectiveness analysis emulating a target trial based on big real world evidence: when to start statin treatment.

JOURNAL OF COMPARATIVE EFFECTIVENESS RESEARCH(2019)

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摘要
Aim: The aim of this project is to describe a causal (counterfactual) approach for analyzing when to start statin treatment to prevent cardiovascular disease using real-world evidence. Methods: We use directed acyclic graphs to operationalize and visualize the causal research question considering selection bias, potential time-independent and time-dependent confounding. We provide a study protocol following the 'target trial' approach and describe the data structure needed for the causal assessment. Conclusion: The study protocol can be applied to real-world data, in general. However, the structure and quality of the database play an essential role for the validity of the results, and database-specific potential for bias needs to be explicitly considered.
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关键词
big data,cardiovascular disease,causal inference,inverse probability of censoring weighting (IPCW),observational study,real-world evidence,statin,study design,target trial,time-dependent confounding
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