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.
StrBCD.sim(n = 1000, cov_num = 2, level_num = c(2, 2),
pr = rep(0.5, 4), p = 0.85)See StrBCD.
the number of patients. The default is 1000.
the number of covariates. The default is 2.
a 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).
a 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 sum of the probabilities for each margin should be 1. The default is rep(0.5, 4), which corresponds to cov_num = 2, and level_num = c(2, 2).
the biased coin probability. p should be larger than 1/2 and less than 1. The default is 0.85.
See StrBCD.
Ma W, Ye X, Tu F, Hu F. carat: Covariate-Adaptive Randomization for Clinical Trials[J]. Journal of Statistical Software, 2023, 107(2): 1-47.
Shao J, Yu X, Zhong B. A theory for testing hypotheses under covariate-adaptive randomization[J]. Biometrika, 2010, 97(2): 347-360.
See StrBCD for allocating patients with complete covariate data; See StrBCD.ui for the command-line user interface.