期刊
BIOMETRIKA
卷 105, 期 3, 页码 745-752出版社
OXFORD UNIV PRESS
DOI: 10.1093/biomet/asy031
关键词
Experimental design; Mahalanobis distance; Noncentral chi-squared distribution; Sequential enrolment
资金
- U.S. Office of Naval Research
- U.S. National Science Foundation
- Google Faculty Fellowship
- National Institutes of Health
- NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES [R01AI102710] Funding Source: NIH RePORTER
The seminal work of Morgan & Rubin (2012) considers rerandomization for all the units at one time. In practice, however, experimenters may have to rerandomize units sequentially. For example, a clinician studying a rare disease may be unable to wait to perform an experiment until all the experimental units are recruited. Our work offers a mathematical framework for sequential rerandomization designs, where the experimental units are enrolled in groups. We formulate an adaptive rerandomization procedure for balancing treatment/control assignments over some continuous or binary covariates, using Mahalanobis distance as the imbalance measure. We prove in our key result that given the same number of rerandomizations, in expected value, under certain mild assumptions, sequential rerandomization achieves better covariate balance than rerandomization at one time.
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