Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
