Abstract
Ivers et al. (2012) have recently stressed the importance to both statistical power and face validity of balancing allocations to study arms on relevant covariates. While several techniques exist (e.g., minimization, pair-matching, stratification), the covariate-constrained randomization (CCR) approach proposed by Moulton (2004) is favored when clusters can be recruited prior to randomization. CCRA V1.0, a macro published by Chaudhary and Moulton (2006), provides a SAS implementation of CCR for a particular subset of possible designs (those with two arms, small numbers of strata and clusters, an equal number of clusters within each stratum, and constraints that can be expressed as absolute mean differences between arms). This paper presents a more comprehensive macro, CCR, that is applicable across a wider variety of designs and provides statistics describing the range of possible allocations meeting the constraints in addition to performing the actual random assignment.
Highlights
In a recent methodological review of allocation techniques for cluster-randomized trials,1 Ivers et al (2012) stressed the importance to both statistical power and face validity of balancing allocations to study arms on relevant Covariates
While several techniques exist, their review favored the covariateconstrained randomization (CCR) approach proposed by Moulton (2004) in situations where clusters can be recruited prior to randomization and the research team has sufficient statistical support
The macro presented here, covariate-constrained randomization (CCR), allows for all these possibilities, as well as running more quickly and flexibly than CCRA V1.0 in large designs and providing statistics describing the range of allowed allocations
Summary
In a recent methodological review of allocation techniques for cluster-randomized trials,1 Ivers et al (2012) stressed the importance to both statistical power and face validity of balancing allocations to study arms on relevant Covariates. The only published program for performing CCR is CCRA V1.0 by Chaudhary and Moulton (2006). This macro can perform CCR under the following conditions: two study arms, small numbers of strata and clusters per stratum Many practical clinical trials where CCR might be desirable may involve unequal-sized strata, differences in counts rather than means (e.g., balancing clusters on binary or categorical variables) or expressed as a proportion of the overall or stratum mean (rather than a fixed number), more than a few clusters and strata, and/or more than two arms. It can be used to randomize individuals rather than clusters; from the macro’s perspective, a cluster is a unit to be randomized
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