PropScrRand (version 1.1.1)

pcr: Propensity-Constrained Randomization

Description

Performs propensity-contstrained randomization on a given data set with measured covariates on all units.

Usage

pcr(x, nTreat, M, m)

Arguments

x

Data frame of covariates.

nTreat

Number of units to be treated.

M

Number of candidate permutations to create.

m

Number of permutations to keep.

Value

treatments

The (approximately) M permutations generated by genPerms.

variance

A vector of the propensity score variances for all M permutations in treatments.

cutoff

The calculated m/M quantile of propensity score variances.

best

The column indices of the permutations in treatments with propensity score variance below cutoff.

Details

Given the parameters, pcr generates M unique permutations by calling genPerms. For each permutation, the empirical propensity scores are computed and the variance returned by getVar. Finally, the m permutations with the smallest propensity score variance are found. The m permutations returned in best can then be used to perform randomization and randomization inference. We suggest M >= 10000 and m/M <= 0.10.

References

Loux, T.M. (2015) Randomization, matching, and propensity scores in the design and analysis of experimental studies with known covariates. Statistics in Medicine. 34(4), 558-570. DOI: 10.1002/sim.6361

Examples

# NOT RUN {
# 1:1 allocation, M small
dat1 = data.frame(x1=rnorm(50),
                  x2=rnorm(50),
                  x3=sample(c('a','b','c'), size=50, replace=TRUE))
trial1 = pcr(x=dat1, nTreat=25, M=500, m=50)

# 1:1 allocation, M large
dat2 = data.frame(x1=rnorm(10),
                  x2=rnorm(10),
                  x3=sample(c('a','b','c'), size=10, replace=TRUE))
trial2 = pcr(x=dat2, nTreat=5, M=200, m=20)

# non-1:1 allocation, M small
dat3 = data.frame(x1=rnorm(50),
                  x2=rnorm(50),
                  x3=sample(c('a','b','c'), size=50, replace=TRUE))
trial3 = pcr(x=dat3, nTreat=35, M=200, m=20)

# non-1:1 allocation, M large
dat4 = data.frame(x1=rnorm(10),
                  x2=rnorm(10),
                  x3=sample(c('a','b','c'), size=10, replace=TRUE))
trial4 = pcr(x=dat4, nTreat=6, M=300, m=30)
# }