Allocates patients to one of two treatments using general covariate-adaptive randomization proposed by Hu Y, Hu F (2012) <doi:10.1214/12-AOS983>, by simulating covariate profiles based on the assumption of independence between covariates and levels within each covariate.
HuHuCAR.sim(n = 1000, cov_num = 2, level_num = c(2, 2),
pr = rep(0.5, 4), omega = NULL, p = 0.85)See HuHuCAR.
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).
a vector of weights at the overall, within-stratum, and within-covariate-margin levels. It is required that at least one element is larger than 0. If omega = NULL (default), the overall, within-stratum, and within-covariate-margin imbalances are weighted with porportions 0.2, 0.3, and 0.5/cov_num for each covariate-margin, respectively, where cov_num is the number of covariates of interest.
the biased coin probability. p should be larger than 1/2 and less than 1. The default is 0.85.
See HuHuCAR.
Hu Y, Hu F. Asymptotic properties of covariate-adaptive randomization[J]. The Annals of Statistics, 2012, 40(3): 1794-1815.
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.
See HuHuCAR for allocating patients with complete covariate data; See HuHuCAR.ui for the command-line user interface.