Generalizing causal inferences from individuals in randomized trials to all trial‐eligible individuals

Autor: Eric J. Tchetgen Tchetgen, Miguel A. Hernán, Issa J Dahabreh, Elizabeth A. Stuart, Sarah E. Robertson
Rok vydání: 2019
Předmět:
FOS: Computer and information sciences
Statistics and Probability
Counterfactual thinking
Pediatrics
medicine.medical_specialty
Average treatment effect
Coronary Artery Disease
01 natural sciences
General Biochemistry
Genetics and Molecular Biology
law.invention
Methodology (stat.ME)
Coronary artery disease
010104 statistics & probability
03 medical and health sciences
Randomized controlled trial
law
Covariate
medicine
Humans
Computer Simulation
Coronary Artery Bypass
0101 mathematics
Statistics - Methodology
Randomized Controlled Trials as Topic
030304 developmental biology
0303 health sciences
General Immunology and Microbiology
business.industry
Patient Selection
Applied Mathematics
General Medicine
medicine.disease
Outcome (probability)
Causality
Treatment Outcome
Causal inference
Chronic Disease
Cohort
General Agricultural and Biological Sciences
business
Zdroj: Biometrics. 75:685-694
ISSN: 1541-0420
0006-341X
DOI: 10.1111/biom.13009
Popis: We consider methods for causal inference in randomized trials nested within cohorts of trial-eligible individuals, including those who are not randomized. We show how baseline covariate data from the entire cohort, and treatment and outcome data only from randomized individuals, can be used to identify potential (counterfactual) outcome means and average treatment effects in the target population of all eligible individuals. We review identifiability conditions, propose estimators, and assess the estimators' finite-sample performance in simulation studies. As an illustration, we apply the estimators in a trial nested within a cohort of trial-eligible individuals to compare coronary artery bypass grafting surgery plus medical therapy vs. medical therapy alone for chronic coronary artery disease.
Databáze: OpenAIRE
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