Document Detail


Balancing continuous covariates based on kernel densities.
MedLine Citation:
PMID:  23270935     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
The balance of important baseline covariates is essential for convincing treatment comparisons. Stratified permuted block design and minimization are the two most commonly used balancing strategies, both of which require the covariates to be discrete. Continuous covariates are typically discretized in order to be included in the randomization scheme. But breakdown of continuous covariates into subcategories often changes the nature of the covariates and makes distributional balance unattainable. In this article, we propose to balance continuous covariates based on kernel density estimations, which keeps the continuity of the covariates. Simulation studies show that the proposed Kernel-Minimization can achieve distributional balance of both continuous and categorical covariates, while also keeping the group size well balanced. It is also shown that Kernel-Minimization is less predictable than stratified permuted block design and minimization. Finally, we apply the proposed method to redesign the NINDS trial, which has been a source of controversy due to imbalance of continuous baseline covariates. Simulation shows that imbalances suchas those observed in the NINDS trial can be generally avoided through the implementation of the new method.
Authors:
Zhenjun Ma; Feifang Hu
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-12-24
Journal Detail:
Title:  Contemporary clinical trials     Volume:  -     ISSN:  1559-2030     ISO Abbreviation:  Contemp Clin Trials     Publication Date:  2012 Dec 
Date Detail:
Created Date:  2012-12-28     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101242342     Medline TA:  Contemp Clin Trials     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2012. Published by Elsevier Inc.
Affiliation:
Department of Statistics, University of Virginia, 22904 Charlottesville, USA.
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