cptest {cvcrand} | R Documentation |
Clustered permutation test for cluster randomized trials
Description
cptest performs a clustered permutation test on the individual-level outcome data for cluster
randomized trials (CRTs). The type of the outcome can be specified by the user to be "continuous"
or
"binary"
.
Linear regression (for outcome type "continuous"
) or logistic regression (for outcome type "binary"
) is applied to the outcome regressed on covariates specified. Cluster residual means are computed. Within the constrained space,
the contrast statistic between the treatment and control arms is created from the randomization schemes and the cluster residual means. The permutation test is then conducted by comparing the contrast statistic for the scheme actually utilized to all other schemes in the constrained space.
Usage
cptest(
outcome,
clustername,
z = NULL,
cspacedatname,
outcometype,
categorical = NULL
)
Arguments
outcome |
a vector specifying the individual-level outcome. |
clustername |
a vector specifying the identification variable of the cluster. |
z |
a data frame of covariates to be adjusted for in the permutation analysis. |
cspacedatname |
gives the path of the csv dataset containing the saved randomization space. This dataset contains the permutation matrix, as well as an indicator variable in the first column indicating which row of the permutation matrix was selected as the final scheme to be implemented in practice. |
outcometype |
the type of regression model that should be run. Options are |
categorical |
a vector specifying categorical (including binary) variables. This can be names of the columns or number indexes of columns, but cannot be both. Suppose there are |
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 about the p-value of the intervention effect from the clustered permutation test
Author(s)
Hengshi Yu <hengshi@umich.edu>, Fan Li <fan.f.li@yale.edu>, John A. Gallis <john.gallis@duke.edu>, Elizabeth L. Turner <liz.turner@duke.edu>
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.
Li, F., Turner, E. L., Heagerty, P. J., Murray, D. M., Vollmer, W. M., & DeLong, E. R. (2017). An evaluation of constrained randomization for the design and analysis of group randomized trials with binary outcomes. Statistics in medicine, 36(24), 3791-3806.
Gallis, J. A., Li, F., Yu, H., Turner, E. L. (In Press). cvcrand and cptest: Efficient design and analysis of cluster randomized trials. Stata Journal.
Gallis, J. A., Li, Fl. Yu, H., Turner, E. L. (2017). cvcrand and cptest: Efficient design and analysis of cluster randomized trials. Stata Conference. https://www.stata.com/meeting/baltimore17/slides/Baltimore17_Gallis.pdf.
Dickinson, L. M., Beaty, B., Fox, C., Pace, W., Dickinson, W. P., Emsermann, C., & Kempe, A. (2015). Pragmatic cluster randomized trials using covariate constrained randomization: A method for practice-based research networks (PBRNs). The Journal of the American Board of Family Medicine, 28(5), 663-672.
Examples
## Not run:
Analysis_result <- cptest(outcome = Dickinson_outcome$outcome,
clustername = Dickinson_outcome$county,
z = data.frame(Dickinson_outcome[ , c("location", "inciis",
"uptodateonimmunizations", "hispanic", "incomecat")]),
cspacedatname = "dickinson_constrained.csv",
outcometype = "binary",
categorical = c("location","incomecat"))
## End(Not run)