SeqAlloc (version 1.0)

SeqAlloc: Sequential Allocation for Prospective Experiments

Description

Simulates results of allocations using complete randomization (CR), random allocation rule (RAR), biased coin design (BCD), permuted block design (PBD), stratified permuted block design (SPBD), covariate-adaptive randomization (CAR), big stick design (BSD), and covariate-adjusted imbalance tolerance (CAIT) designs. The order of the prospective enrollees is permuted for a preset number of iterations; for each iteration, the allocations are determined for each of the methods listed above. The allocations are then evaluated for balance on the covariates and for predictability (i.e., how well an observer could guess the next treatment assignment).

Usage

SeqAlloc(xmat, carwt, strata = NULL, blksize, pbcd, pcar, bsdtol, caittol,  niter, seed = 12345)

Arguments

xmat

matrix or data frame of covariates for prospective enrollees in the experiment. This matrix is to be used in CAR/CAIT methods, and should include strata or marginals of strata as columns

carwt

vector of weights to be used for CAR and CAIT methods

strata

vector of planned strata for study (if none, should be NULL)

blksize

vector of block sizes for PBDs and SPBDs

pbcd

probability for biased coin design (BCD) method

pcar

probability for CAR method

bsdtol

tolerance (d value) for BSD method

caittol

tolerance (d value) for CAIM method

niter

number of iterations for simulation

seed

random number seed, allows the allocation to be reproduced later

Value

List containing summary statistics (minimum, 25th percentile, median, mean, 75th percentile, 90th percentile, 95th percentile, maximum) for evaluation measures, including AI, Rsquared, MAIC, WAIC, perccorr, and perccorr_strat.

References

Blackwell, David and J. L. Hodges (1957). Design for the Control of Selection Bias. Annals of Mathematical Statistics 28: 449-460.

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.

Rosenberger, W. F. and Lachin, J. M. (2004). Randomization in Clinical Trials: Theory and Practice. New York: Wiley.

Examples

sampsize <- 200
percent <- c(0.5,0.8,0.2,0.4)
set.seed(200)

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[,2]*2 + xmat[,4] + 1

SeqAlloc(xmat,carwt=c(.4,.3,.2,.1),strata=strat_factor,blksize=c(2,6),
         pbcd=.7,pcar=.8,bsdtol=2,caittol=5,niter=10, seed = 20850)