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

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,...

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Published in:Journal of comparative effectiveness research 2019-09, Vol.8 (12), p.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: Article
Language:English
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
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creator Kuehne, Felicitas
Jahn, Beate
Conrads-Frank, Annette
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Urach, Christoph
Gyimesi, Michael
Murray, Eleanor J
Danaei, Goodarz
Gaziano, Thomas A
Pandya, Ankur
Siebert, Uwe
description 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.
doi_str_mv 10.2217/cer-2018-0103
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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
title Guidance for a causal comparative effectiveness analysis emulating a target trial based on big real world evidence: when to start statin treatment
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