caralloc {SeqAlloc}

Sequential Allocation Using Covariate Adaptive Randomization

Description

Performs the sequential allocation for the covariate-adjusted randomization (CAR) method of allocating observations in a randomized experiment.

Usage

caralloc(xmat, carwt, p, tol)

Arguments

xmat

matrix or data frame of covariates for prospective enrollees in the experiment.

carwt

vector of weights

p

probability the next unit should be allocated to the experiment arm that currently has fewer observations. For CAR, use 0.5 < p < 1.

tol

tolerance for deviation from equal allocation. For CAR, set tol to be a small value, say 0.01. For CAIM, set tol to be the imbalance tolerance (d).

Value

Vector with the allocation to treatment (denoted by 1) and control (denoted by 0)

Author(s)

Xiaoshu Zhu [e-mail] and Sharon Lohr

References

Lohr, S. and X. Zhu (2015). Randomized Sequential Individual Assignment in Social Experiments: Evaluating the Design Options Prospectively. Sociological Methods and Research. [Advance online publication: December 27, 2015] doi: 10.1177/0049124115621332.

Pocock, S. J. and R. Simon (1975). Sequential Treatment Assignment with Balancing for Prognostic Factors in A Controlled Clinical Trial. Biometrics 31: 103-115.

Examples

sampsize <- 200
percent <- c(0.5,0.8,0.2,0.4)
carwt <- c(.4,.3,.2,.1)

set.seed(5798)

xmat <- matrix(rbinom(sampsize*length(percent),1,rep(percent,sampsize)),
              nrow=sampsize,ncol=length(percent),byrow=TRUE)
colnames(xmat) = c("C1","C2","C3","C4")
strat_factor = xmat[,1]*4 + xmat[,2]*2 + xmat[,4] + 1

caralloc(xmat,carwt,1,3)