Accounting for correlation and compliance in cluster randomized trials.
This paper discusses causal inference with survival data from cluster randomized trials. It is argued that cluster randomization carries the potential for post-randomization exposures which involve differentially selective compliance between treatment arms, even for an all or nothing exposure at the individual level. Structural models can be employed to account for post-randomization exposures, but should not ignore clustering. We show how marginal modelling and random effects models allow to adapt structural estimators to account for clustering. Our findings are illustrated with data from a vitamin A trial for the prevention of infant mortality in the rural plains of Nepal.
Loeys
T
T
Department of Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281, Building S9, 9000 Ghent, Belgium. tom.loeys@rug.ac.be
Vansteelandt
S
S
Goetghebeur
E
E
D016000:Cluster Analysis; D006801:Humans; D007223:Infant; D007226:Infant Mortality; D008954:Models, Biological; D015233:Models, Statistical; D009390:Nepal; D010349:Patient Compliance; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D014801:Vitamin A / Q000008:administration & dosage
D014801:Vitamin A