Abstract
In practice it is very common for sets of covariate data to be incomplete, however, there is little work on balancing treatment assignment over partially observed covariates in literature. In this paper, we propose a new covariate-adaptive design to address this problem, which constructs imbalance measure by weighted absolute differences. Theoretical results show that overall imbalance, observed margin imbalance and fully observed stratum imbalance are all bounded in probability as the sample size increases, at the same time, restored margin imbalance and restored stratum imbalance increase with the rate n. Finally, we confirm theoretical findings and compare the proposed design with DBAI (Liu et al., 2015) through simulations.
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.
