## Covariate-adjusted Biased Coin Design with Covariate Data Generating Mechanism

### Description

Allocates patients to one of two treatments based on the covariate-adjusted biased coin design as proposed by Baldi Antognini A, Zagoraiou M (2011) <doi:10.1093/biomet/asr021>, by simulating the covariates-profile under the assumption of independence between covariates and levels within each covariate.

### Usage

AdjBCD.sim(n = 1000, cov_num = 2, level_num = c(2, 2),
pr = rep(0.5, 4), a = 3)


### Arguments

 n the number of patients. The default is 1000. cov_num the number of covariates. The default is 2. level_num 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). pr 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 a design parameter governing the degree of randomness. The default is 3.

### Details

See AdjBCD.

### Value

See AdjBCD.

### References

Baldi Antognini A, Zagoraiou M. The covariate-adaptive biased coin design for balancing clinical trials in the presence of prognostic factors[J]. Biometrika, 2011, 98(3): 519-535.

See AdjBCD for allocating patients with complete covariate data; See AdjBCD.ui for the command-line user interface.