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A SAS macro for constrained randomization of group-randomized designs.

M Ashraf Chaudhary1, Lawrence H Moulton

  • 1Department of International Health, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA. mchaudha@jhsph.edu

Computer Methods and Programs in Biomedicine
|July 28, 2006
PubMed
Summary

Group-randomized trials (GRTs) are essential when individual randomization is not feasible. This study introduces a SAS macro to improve covariate balance in stratified GRTs, enhancing randomization quality.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Epidemiology

Background:

  • Group-randomized trials (GRTs) are crucial when individual randomization is impractical or insufficient for estimating key parameters.
  • GRTs often face challenges like small group numbers and geographical correlations, increasing the risk of biased randomization.
  • Covariate imbalance can significantly impact the validity and power of GRTs.

Purpose of the Study:

  • To present a SAS macro for generating balanced treatment allocations in two-arm stratified group-randomized designs.
  • To ensure relevant baseline covariates are balanced across treatment groups in GRTs.
  • To provide a practical tool for improving the methodological rigor of GRTs.

Main Methods:

  • Development of a SAS macro for constrained randomization in stratified GRTs.

Related Experiment Videos

  • The macro enforces balance on specified baseline covariates.
  • The macro was applied to two illustrative examples of group-randomized designs.

    • Main Results:

    • The SAS macro successfully allocates treatment conditions while ensuring covariate balance.
    • Demonstrated the macro's utility in practical group-randomized study scenarios.
    • The proposed method enhances the quality of randomization in stratified GRTs.

    Conclusions:

    • The developed SAS macro offers a robust solution for achieving covariate balance in stratified group-randomized trials.
    • This tool can mitigate risks associated with poor randomization outcomes in GRTs.
    • Implementing this macro can lead to more reliable and statistically sound research findings from GRTs.