Allocates patients generated by simulating covariates-profile under the assumption of independence between covariates and levels within each covariate, to one of two treatments based on the \(D_A\)-optimal biased coin design in the presence of prognostic factors, as proposed by Atkinson A C (1982) <doi:10.2307/2335853>.
DoptBCD.sim(n = 1000, cov_num = 2, level_num = c(2, 2),
pr = rep(0.5, 4))See DoptBCD.
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).
See DoptBCD.
Atkinson A C. Optimum biased coin designs for sequential clinical trials with prognostic factors[J]. Biometrika, 1982, 69(1): 61-67.
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 DoptBCD for allocating patients with complete covariate data; See DoptBCD.ui for the command-line user interface.