cvcrand (version 0.0.1)

cptest: Clustered permutation test for cluster randomized trials

Description

cptest performs clustered permutation test on the individual oucome for cluster randomized trials (CRTs). The type of the outcome can be specified by the user to be "continuous" or "binary".

With specified outcome type being "continuous" or "binary", linear regression or logistic regression is applied on the outcome and the covariates specified for all individuals. Cluster residual means are collected. With the constrained space, the contrast statistic is created from the schemes and the cluster residual means. The permutation test is then conducted on the contrast statistic for the scheme actually utilized.

Usage

cptest(outcome, id, x, cspacedatname, outcometype, categorical = NULL)

Arguments

outcome

a vector specifying the individual outcome

id

a vector specifying cluster name

x

a matrix or a data frame specifying the individual-level covariates to analyze

cspacedatname

the csv dataset containing the saved randomization space. This dataset contains the permutation matrix, as well as a variable indicating which row of the permutation matrix was saved as the final schemes.

outcometype

the type of regression model that should be run. Options are "continuous" for linear regression fit and "binary" for logistic regression fit.

categorical

a vector specifying categorical (including binary) variables. This can be names of the columns or numbers of indexes of columns)

Value

FinalScheme the final scheme in the permutation matrix

pvalue the p-value of the intervention effect from the clustered permutation test

pvalue_statement the statement of the p-value of the intervention effect from the clustered permutation test

References

Gail, M.H., Mark, S.D., Carroll, R.J., Green, S.B. and Pee, D., 1996. On design considerations and randomization based inference for community intervention trials. Statistics in medicine, 15(11), pp.1069-1092.

Li, F., Lokhnygina, Y., Murray, D.M., Heagerty, P.J. and DeLong, E.R., 2016. An evaluation of constrained randomization for the design and analysis of group randomized trials. Statistics in medicine, 35(10), pp.1565-1579.

Examples

# NOT RUN {
Analysis_result <- cptest(outcome = Dickinson_outcome$outcome,
                          id = Dickinson_outcome$county,
                          x = data.frame(Dickinson_outcome[, c(-1, -7)]),
                          cspacedatname="dickinson_constrained.csv",
                          outcometype="binary",
                          categorical=c("location","incomecat"))
# }
# NOT RUN {
# }