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

1. 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

2. 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

3. 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

4. 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

5. 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

6. 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

7. Thank you Biostatistics Core Meeting: LSHTM London