HuHuCAR.sim {carat} | R Documentation |
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)
n |
the number of patients. The default is |
cov_num |
the number of covariates. The default is |
level_num |
a vector of level numbers for each covariate. Hence the length of |
pr |
a vector of probabilities. Under the assumption of independence between covariates, |
omega |
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 |
p |
the biased coin probability. |
See HuHuCAR
.
See HuHuCAR
.
Hu Y, Hu F. Asymptotic properties of covariate-adaptive randomization[J]. The Annals of Statistics, 2012, 40(3): 1794-1815.
See HuHuCAR
for allocating patients with complete covariate data; See HuHuCAR.ui
for the command-line user interface.