The Efficient Covariate-Adaptive Design for high-order balancing of quantitative and qualitative covariates

Alessandro Baldi Antognini, Rosamarie Frieri (), Maroussa Zagoraiou () and Marco Novelli ()
Additional contact information
Rosamarie Frieri: University of Bologna
Maroussa Zagoraiou: University of Bologna
Marco Novelli: University of Bologna

Statistical Papers, 2024, vol. 65, issue 1, No 2, 19-44

Abstract: Abstract In the context of sequential treatment comparisons, the acquisition of covariate information about the statistical units is crucial for the validity of the trial. Furthermore, balancing the assignments among covariates is of primary importance, since the potential imbalance of the covariate distributions across the groups can severely undermine the statistical analysis. For this reason, several covariate-adaptive randomization procedures have been suggested in the literature, but most of them only apply to categorical factors. In this paper we propose a new class of rules, called the Efficient Covariate-Adaptive Design, which is high-order balanced regardless of the number of factors and their nature (qualitative and/or quantitative), also accounting for every order covariate effects and interactions. The suggested procedure performs very well, is flexible and simple to implement. The advantages of our proposal are also analyzed via simulations and its finite sample properties are compared with those of other well-known rules, by also including the redesign of a real clinical trial.

Keywords: Biased coin design; Markov chains; Minimization methods; Stratified randomization (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00362-022-01381-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stpapr:v:65:y:2024:i:1:d:10.1007_s00362-022-01381-1

Ordering information: This journal article can be ordered from
http://www.springer. ... business/journal/362

DOI: 10.1007/s00362-022-01381-1

Access Statistics for this article

Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller

More articles in Statistical Papers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().