k‐Resolution sequential randomization procedure to improve covariates balance in a randomized experiment

Balancing allocation of assigning units to two treatment groups to minimize the allocation differences is important in biomedical research. The complete randomization, rerandomization, and pairwise sequential randomization (PSR) procedures can be employed to balance the allocation. However, the firs...

Full description

Saved in:
Bibliographic Details
Published in:Statistics in medicine 2021-11, Vol.40 (25), p.5534-5546
Main Authors: Long, Mingya, Sun, Liuquan, Li, Qizhai
Format: Article
Language:English
Subjects:
asymptotic variance
balancing allocation
clinical
complete randomization
Mahalanobis distance
Medical statistics
pairwise sequential randomization
rerandomization
Statistical analysis
treatment effect
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Balancing allocation of assigning units to two treatment groups to minimize the allocation differences is important in biomedical research. The complete randomization, rerandomization, and pairwise sequential randomization (PSR) procedures can be employed to balance the allocation. However, the first two do not allow a large number of covariates. In this article, we generalize the PSR procedure and propose a k‐resolution sequential randomization (k‐RSR) procedure by minimizing the Mahalanobis distance between both groups with equal group size. The proposed method can be used to achieve adequate balance and obtain a reasonable estimate of treatment effect. Compared to PSR, k‐RSR is more likely to achieve the optimal value theoretically. Extensive simulation studies are conducted to show the superiorities of k‐RSR and applications to the clinical synthetic data and GAW16 data further illustrate the methods.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.9139