Allocates patients to one of two treatments using Shao's method proposed by Shao J, Yu X, Zhong B (2010) <Doi:10.1093/biomet/asq014>, by simulating covariate profiles under the assumption of independence between covariates and levels within each covariate.
# S3 method for carandom
StrBCD.sim(n = 1000, cov_num = 2, level_num = c(2, 2),
pr = rep(0.5, 4), p = 0.85)the number of patients. The default is 1000.
the number of covariates. The default is 2.
the vector of level numbers for each covariate. Hence the length of level_num should be equal to the number of covariates. The default is c(2, 2).
the vector of probabilities. Under the assumption of independence between covariates, pr is a vector containing probabilities for each level of each covariate. The length of pr should correspond to the number of all levels, and the vector sum of pr should be equal to cov_num. The default is pr = rep(0.5, 4) (default), which implies that cov_num = 2 and level_num = c(2, 2).
the probability of assigning one patient to treatment 1. p should be larger than 1/2 to obtain balance. The default is 0.85.
See StrBCD.
See StrBCD.
See StrBCD for allocating patients with complete covariate data; See StrBCD.ui for the command-line user interface.