# Minirand: Minimization randomization to k treatment groups In Minirand: Minimization Randomization

## Description

The function is used to generate treatment assignment by minimization algorithms.

## Usage

 ```1 2``` ```Minirand(covmat = covmat, j, covwt = covwt, ratio = ratio, ntrt = ntrt, trtseq = trtseq, method = "Range", result = res, p) ```

## Arguments

 `covmat` matrix or data frame of covariate factors `j` the jth subject in the randomization sequence `covwt` vector of weights of the covaraite factors `ratio` vector of randomization ratios for each treatment `ntrt` numeric number of treatment groups `trtseq` vector of a sequence of treatment groups `method` the method or algorithm for the minimization randomization `result` the treatment assignments in subjetcs achieved so far `p` the high probability for new assignment

## Value

treatment assignment for the jth subject

## References

Pocock and Simon (1975), Sequential Treatment Assignment with Balancing for Prognostic Factors in the Controlled Clinical Trial. Biometrics; 103-115.

Jin, Polis, and Hartzel (2019), "Algorithms for minimization randomization and the implementation with an R package". Communications in Statistics-Simulation and Computation; May 2019.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```ntrt <- 3 nsample <- 120 trtseq <- c(1, 2, 3) ratio <- c(2, 2, 1) c1 <- sample(seq(1, 0), nsample, replace = TRUE, prob = c(0.4, 0.6)) c2 <- sample(seq(1, 0), nsample, replace = TRUE, prob = c(0.3, 0.7)) c3 <- sample(c(2, 1, 0), nsample, replace = TRUE, prob = c(0.33, 0.2, 0.5)) c4 <- sample(seq(1, 0), nsample, replace = TRUE, prob = c(0.33, 0.67)) covmat <- cbind(c1, c2, c3, c4) # generate the matrix of covariate factors for the subjects # label of the covariates colnames(covmat) = c("Gender", "Age", "Hypertension", "Use of Antibiotics") covwt <- c(1/4, 1/4, 1/4, 1/4) #equal weights res <- rep(100, nsample) # result is the treatment needed from minimization method #gernerate treatment assignment for the 1st subject res = sample(trtseq, 1, replace = TRUE, prob = ratio/sum(ratio)) for (j in 2:nsample) { # get treatment assignment sequentiall for all subjects res[j] <- Minirand(covmat=covmat, j, covwt=covwt, ratio=ratio, ntrt=ntrt, trtseq=trtseq, method="Range", result=res, p = 0.9) } trt1 <- res #Display the number of randomized subjects at covariate factors balance1 <- randbalance(trt1, covmat, ntrt, trtseq) balance1 totimbal(trt = trt1, covmat = covmat, covwt = covwt, ratio = ratio, ntrt = ntrt, trtseq = trtseq, method = "Range") ```

Minirand documentation built on Jan. 26, 2020, 9:10 a.m.