Type: Article
Publication Date: 2017-01-01
Citations: 27
DOI: https://doi.org/10.18637/jss.v077.c01
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 covariateconstrained 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.