If you use this software, please cite this article:
Greene, E.J. A SAS macro for covariate-constrained randomization of general cluster-randomized and unstratified designs. Journal of Statistical Software 2017, 77(CS1):1-20.
This SAS macro for covariate-constrained randomization is released under the GNU Lesser General Public License (see lgpl.txt and gpl.txt).
The sample output in ccr_example_output.pdf can be generated from the macro and the dataset ccr_example_data.sas7bdat with the command
%ccr(ccr_example_data, sid, gid,
lev1 lev2 lev3 covar,
d d d c,
s1 s1 s1 mf.15,
s1 s1 s1 m200,
seed=22571);This usage information is lifted from the macro comments; for a more user-friendly article, see here.
/****************************/
/* ARGUMENTS AND PARAMETERS */
/****************************/
/* REQUIRED ARGUMENTS:
dataset = the input dataset, which should have one observation per cluster
stratid = the variable in the dataset identifying which stratum a cluster
is in; can leave blank for an unstratified design
clustid = the variable in the dataset identifying each cluster
x = list of covariate variables
vtype = 'd' -> dummy covariate
(display checks as frequency tables of arms & differences)
'c' -> continuous/many-valued covariate
(display checks as mean/min/q1/median/q3/max of difference)
slimit = stratum balancing criteria; set to any for unstratified design
olimit = overall balancing criteria
formatting for criteria: {type}{difference} -or- ANY
ANY imposes no constraint (allows for constraining only at stratum
or overall level)
{type} = 'm' for mean or 's' for sum
if {difference} starts with f, it's treated as an allowable
fraction of the total
FOR EXAMPLE:
s100 -> sums must differ by no more than 100
mf.5 -> means must differ by no more than half the overall mean
NOTE: values in vtype, slimit, and olimit should be separated by
whitespace (CCR_V1.0 used asterisks)
OPTIONAL PARAMETERS:
arms = the number of arms to allocate to [default: 2]
armsize = the variables in the dataset identifying how many clusters in a
stratum to allocate to each arm
if not set, clusters are split as evenly as possible between arms
(at both levels)
ssample = number of possible stratum allocations to sample and check in
each stratum [default: 100,000]
osample = number of combinations of acceptable stratum allocations to
sample and check [default: 100,000]
if not set, all combinations will be generated and checked
seed = seed for sampling allocations and choosing final randomization
if not set, SAS will seed from the system clock
(rand function and proc surveyselect default behavior)
select = # of final randomizations to display [default: 1]
samearmhi = percentage at which clusters appear in the same arm "a lot"
[default: 75]
samearmlo = percentage at which clusters appear in the same arm "rarely"
[default: undefined -> 100/(2*arms), which is half chance]
verbose controls how much is sent to output and log
(though everything is available afterwards in temporary datasets
regardless of setting)
0/false -> only print summaries and checks
1/true -> print lists of allocations that satisfy the constraints,
plus the final randomization
2 -> print lists of all possible within-stratum allocations
3 -> print lists of all possible (or all sampled) overall allocations
setting binomsig prints all pairs of clusters whose rates of appearance in
the same arm differ significantly from chance (parameter sets alpha)
setting debug prints a lot of macro variables to the log
setting sizecheck to 0 disables checking whether a sample size
is larger than the number of overall allocations
*/