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

• Published in: Journal of comparative effectiveness research Vol. 8; no. 12; pp. 1013 - 1025
• Main Authors: Kuehne, Felicitas, Jahn, Beate, Conrads-Frank, Annette, Bundo, Marvin, Arvandi, Marjan, Endel, Florian, Popper, Niki, Endel, Gottfried, Urach, Christoph, Gyimesi, Michael, Murray, Eleanor J, Danaei, Goodarz, Gaziano, Thomas A, Pandya, Ankur, Siebert, Uwe
• Format: Journal Article
• Language: English
• Published: England Future Medicine Ltd 01.09.2019
• Subjects: big data; Cardiology; Cardiovascular disease; causal inference; Cholesterol; Clinical trials; Data analysis; Decision making; Diabetes; Disease prevention; Heart; inverse probability of censoring weighting (IPCW); Kidney diseases; Mortality; Observational studies; observational study; real-world evidence; Researchers; statin; Statins; Statistical methods; study design; target trial; time-dependent confounding; cardiovascular disease; inverse probability of censoring weighting (IPCW); real-world evidence; time-dependent confounding; statin; target trial; big data; causal inference; study design; observational study
• ISSN: 2042-6305; 2042-6313
• DOI: 10.2217/cer-2018-0103

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. 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. 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.