cvcrand and cptest: Commands for efficient design and analysis of cluster randomized trials using constrained randomization and permutation tests
John A. Gallis
Duke University
Department of Biostatistics
and Bioinformatics
Duke Global Health Institute
Durham, NC
[email protected]
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Fan Li
Duke University
Department of Biostatistics
and Bioinformatics
Durham, NC
[email protected]
|
Hengshi Yu
University of Michigan
Department of Biostatistics
Ann Arbor, MI
[email protected]
|
Elizabeth L. Turner
Duke University
Department of Biostatistics
and Bioinformatics
Duke Global Health Institute
Durham, NC
[email protected]
|
Abstract. Cluster randomized trials (CRTs), where clusters (for example, schools
or clinics) are randomized to comparison arms but measurements are taken on individuals,
are commonly used to evaluate interventions in public health, education,
and the social sciences. Because CRTs typically involve a small number of clusters
(for example, fewer than 20), simple randomization frequently leads to baseline imbalance
of cluster characteristics across study arms, threatening the internal validity
of the trial. In CRTs with a small number of clusters, classic approaches to balancing
baseline characteristics—such as matching and stratification—have several
drawbacks, especially when the number of baseline characteristics the researcher
desires to balance is large (Ivers et al., 2012, Trials 13: 120). An alternative design
approach is covariate-constrained randomization, whereby a randomization
scheme is randomly selected from a subset of all possible randomization schemes
based on the value of a balancing criterion (Raab and Butcher, 2001, Statistics in
Medicine 20: 351–365). Subsequently, a clustered permutation test can be used
in the analysis, which provides increased power under constrained randomization
compared with simple randomization (Li et al., 2016, Statistics in Medicine 35:
1565–1579). In this article, we describe covariate-constrained randomization and
the permutation test for the design and analysis of CRTs and provide an example
to demonstrate the use of our new commands cvcrand and cptest to implement
constrained randomization and the permutation test.
View all articles by these authors:
John A. Gallis, Fan Li, Hengshi Yu, Elizabeth L. Turner
View all articles with these keywords:
cvcrand, cptest, covariate-constrained randomization, cluster randomized trials, permutation test
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