Sas macro for constrained randomization balancing covariates in group randomized trials
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SAS Macro for Constrained Randomization: Balancing covariates in Group Randomized Trials. Ashraf Chaudhary, Ph.D. & Larry Moulton, Ph.D. Department of International Health Division of Disease Control and Prevention Johns Hopkins University Bloomberg School of Public Health.

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SAS Macro for Constrained Randomization: Balancing covariates in Group Randomized Trials

Ashraf Chaudhary, Ph.D.

& Larry Moulton, Ph.D.

Department of International Health

Division of Disease Control and Prevention

Johns Hopkins University Bloomberg School of Public Health


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Why Constrained Randomization?

  • In individually randomized designs, larger sample sizes ensure balance on key variables between the trial arms.

  • Group randomized trials are typically small with perhaps only 4-20 groups to be randomized.

  • The groups are usually contiguous and more homogenous relevant to the groups farther apart.

  • Spatial correlation patterns are more difficult to detect in human communities.

Biostatistics Core Meeting: LSHTM London


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Why Constrained Randomization?

  • Group level randomization may lead to substantial imbalance across the trial arms.

  • Group randomized trials are therefore susceptible to the ill effects of an ‘unlucky’ or ‘bad’ randomization.

  • But the question here is how to randomize a small number of groups so as to avoid an ‘unlucky’ or ‘bad’ randomization.

Biostatistics Core Meeting: LSHTM London


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Covariate-Based Constrained Randomization

  • Randomizing the groups to, say, ‘intervention’ and ‘control’ study arms so as to achieve a balance on some baseline covariates between the trial arms.

Biostatistics Core Meeting: LSHTM London


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SAS Macro - Steps

  • Generates all possible randomizations by forming combinations of groups in each stratum.

  • Computes means of covariates for each randomization in each arm and combine the data for the two arms.

  • Shortlists the randomizations that satisfy balancing criteria.

  • Generates a large number of samples, say, 100, by picking one randomization ‘randomly’ from each stratum.

  • Retains only those samples that meet the sample level balancing criteria.

  • As a check, counts the number of times a group appears with another group in the same study arm.

  • Selects one randomization at random from all the short listed samples.

Biostatistics Core Meeting: LSHTM London


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SAS Macro - Inputs

  • SAS dataset with the following variables

    • s: Stratum ID

    • group: Group ID

    • x1, x2, x3, …: Covariates

    • r: Number to be randomized to, say, study arm 1

  • SAS Macro parameters

    • Number of covariates

    • Names of covariates

    • Randomization level minimum acceptable differences between treatment arms for each covariate

    • Overall sample level minimum acceptable differences between treatment arms for each covariate

    • Seed for random selection.

  • Biostatistics Core Meeting: LSHTM London


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    Thank you

    Biostatistics Core Meeting: LSHTM London