help block_and_cluster_raVersion 1.1

Title

block_and_cluster_ra -- A random assignment procedure in which units are assigned as clusters and clusters are nested within blocks.

Syntax

block_and_cluster_ra [treatvar] ,
cluster_var(var)
block_var(var)
[block_m(numlist)
block_m_each(matrix)
block_prob_each(matrix)
block_prob(numlist)
m(num)
prob(num)
prob_each(numlist)
num_arms(num)
condition_names(string)
replace
skip_check_inputs]

Description

block_and_cluster_ra A random assignment procedure in which units are assigned as clusters and clusters are nested within blocks.

Options

treatvar The name of the treatment variable, you want this command to generate. If left unspecified the resulting variable is called "assignment" by default.

clustervar(var) The variable that indicates which cluster each unit belongs to. Can be a string or numeric variable.

block_var(var) An existing variable that indicates which block each unit belongs to. Can be a string or numeric variable.

m(num) Use for a two-arm design in which the scalar m describes the fixed number of clusters to assign in each block. This number does not vary across blocks.

block_m(numlist) Use for a two-arm design in which the numlist block_m describes the number of clusters to assign to treatment within each block. block_m must be a numeric list
that is as long as the number of blocks.

block_m_each(matrix) Use for a multi-arm design in which the values of block_m_each determine the number of clusters assigned to each condition.
block_m_each must be a matrix with the same number of rows as blocks and the same number of columns as treatment arms. Cell entries are the number of clusters to be
assigned to each treatment arm within each block. The rows should respect the ordering of the blocks as determined by a tabulate command. The columns should
be in the order of condition_names, if specified.

block_prob_each(matrix) Use for a multi-arm design in which the values of block_prob_each determine the probabilties of assignment to each treatment condition.
block_prob_each must be a matrix with the same number of rows as blocks and the same number of columns as treatment arms. Cell entries are the probabilites of assignment to treatment
within each block. The rows should respect the ordering of the blocks as determined by a tabulate command.
Use only if the probabilities of assignment should vary by block, otherwise use prob_each. Each row of block_prob_each must sum to 1.

block_prob(numlist) Use for a two-arm design in which block_prob describes the probability of assignment to treatment within each block.
Differs from prob in that the probability of assignment can vary across blocks.

prob(num) Use for a two-arm design in which either floor(N_block*prob) or ceiling(N_block*prob) clusters are assigned to treatment within each block.
The probability of assignment to treatment is exactly prob because with probability 1-prob, floor(N_block*prob) clusters will be assigned to treatment and with probability prob,
ceiling(N_block*prob) clusters will be assigned to treatment. prob must be a real number between 0 and 1 inclusive.

prob_each(numlist) Use for a multi-arm design in which the values of prob_each determine the probabilties of assignment to each treatment condition.
prob_each must be a list of numbers giving the probability of assignment to each condition. All entries must be nonnegative real numbers between 0 and 1 inclusive and
the total must sum to 1. Because of integer issues, the exact number of clusters assigned to each condition may
differ (slightly) from assignment to assignment, but the overall probability of assignment is exactly prob_each.

num_arms(num) The number of treatment arms. If unspecified, num_arms will be determined from the other arguments.

condition_names(string) A string list giving the names of the treatment groups. If unspecified, the treatment groups will be named 0 (for control) and 1 (for treatment)
in a two-arm trial and 1, 2, 3, in a multi-arm trial.
An execption is a two-group design in which num_arms is set to 2, in which case the condition names are 1 and 2, as in a multi-arm trial with two arms.

replace If treatvar exists in dataset, the command replaces it.

skip_check_inputs Suppress error checking.

Examples

. set obs 350

. gen cluster=runiformint(1,26)

. gen block="block_1" if cluster>0 & cluster<=5

. replace block="block_2" if cluster>5 & cluster<=10

. replace block="block_3" if cluster>10 & cluster<=15

. replace block="block_4" if cluster>15 & cluster<=20

. replace block="block_5" if cluster>20 & cluster<=26

. block_and_cluster_ra, block_var(block) cluster_var(cluster)

. block_and_cluster_ra, block_var(block) cluster_var(cluster) num_arms(3) replace

. block_and_cluster_ra, block_var(block) cluster_var(cluster) prob_each(.2 .5 .3) replace

. matrix define block_m_each=(2, 3\1,4\3,2\2,3\4,1)

. block_and_cluster_ra, block_var(block) cluster_var(cluster) block_m_each(block_m_each) replace

Authors

John Ternovski

Alex Coppock

Yale University

john.ternovski@yale.edu