PocSimMIN.sim {carat}

Pocock and Simon's Method in the Two-Arms Case with Covariate Data Generating Mechanism

Description

Allocates patients to one of two treatments using Pocock and Simon's method proposed by Pocock S J, Simon R (1975) <doi:10.2307/2529712>, by simulating covariate profiles under the assumption of independence between covariates and levels within each covariate.

Usage

PocSimMIN.sim(n = 1000, cov_num = 2, level_num = c(2, 2), 
              pr = rep(0.5, 4), weight = NULL, p = 0.85)

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).

weight

a vector of weights for within-covariate-margin imbalances. It is required that at least one element is larger than 0. If weight = NULL (default), the within-covariate-margin imbalances are weighted with an equal proportion, 1/cov_num, for each covariate-margin.

p

the biased coin probability. p should be larger than 1/2 and less than 1. The default is 0.85.

Details

See PocSimMIN.

Value

See PocSimMIN.

References

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.

Pocock S J, Simon R. Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trial[J]. Biometrics, 1975: 103-115.

See Also

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