# mergedblocksmulti

0th

Percentile

##### Merged block randomization for multiple strata

Function to carry out merged block randomization, for multiple strata.

##### Usage
mergedblocksmulti(K, n, ratio = c(1, 1),
labels = as.character(1:length(ratio)))
##### Arguments
K

The number of strata.

n

The number of subjects to randomize. May be given as a single number, for the same number of subjects per stratum, or as a vector or length K in case the desired sample size varies per stratum

ratio

The desired randomization ratio, given as a vector. Default is 1:1 randomization, but more groups or unequal ratios are possible as well. E.g. for 1:1:2 randomization, use c(1, 1, 2).

labels

The labels for the assignments, given as a vector, e.g. c("treatment", "placebo"). The length of labels should match the length of ratio. Default is to use numeric labels.

##### Value

Allocation of the subjects, given as a dataframe, with one column per stratum. Padded with NAs in case of different sample sizes per stratum.

##### References

S.L. van der Pas (2019). Merged block randomisation: a novel randomisation procedure for small clinical trials. Clinical Trials. Pages tba.

mergedblocks for a version for a single stratum.

##### Aliases
• mergedblocksmulti
##### Examples
# NOT RUN {
#Four strata, randomize 20 patients for each stratum, 1:1 allocation,
#with labels "0" and "1".
mergedblocksmulti(K = 4, n = 20)

#Three strata, randomize 30, 40 and 50 patients for each stratum,
#1:2 allocation, with labels "placebo" and "treatment".
mergedblocksmulti(K = 3, n = c(30, 40, 50), ratio = c(1, 2), labels = c("placebo", "treatment"))

# }

Documentation reproduced from package mergedblocks, version 1.0.0, License: GPL-3

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