Nothing
## ----start,echo=FALSE,results="hide"------------------------------------------
library(cvcrand)
## ---- echo=FALSE, results='asis'----------------------------------------------
knitr::kable(Dickinson_design[ , 1:6])
## ---- echo=FALSE, results='asis'----------------------------------------------
knitr::kable(Dickinson_design[ , 7:11])
## ----cvrall, fig.keep="all", fig.width = 7, fig.height=4----------------------
Design_result <- cvrall(clustername = Dickinson_design$county,
balancemetric = "l2",
x = data.frame(Dickinson_design[ , c("location", "inciis",
"uptodateonimmunizations", "hispanic", "incomecat")]),
ntotal_cluster = 16,
ntrt_cluster = 8,
categorical = c("location", "incomecat"),
###### Option to save the constrained space #####
# savedata = "dickinson_constrained.csv",
bhist = TRUE,
cutoff = 0.1,
seed = 12345)
## ----set-options1, echo=FALSE, fig.keep="all", fig.width = 7, fig.height=4------------------------
options(width = 100)
## ---- fig.keep="all", fig.width = 7, fig.height=4-------------------------------------------------
# the balance metric used
Design_result$balancemetric
# the allocation scheme from constrained randomization
Design_result$allocation
# the histogram of the balance score with respect to the balance metric
Design_result$bscores
# the statement about how many clusters to be randomized to the intervention and the control arms respectively
Design_result$assignment_message
# the statement about how to get the whole randomization space to use in constrained randomization
Design_result$scheme_message
# the statement about the cutoff in the constrained space
Design_result$cutoff_message
# the statement about the selected scheme from constrained randomization
Design_result$choice_message
# the data frame containing the allocation scheme, the clustername as well as the original data frame of covariates
Design_result$data_CR
# the descriptive statistics for all the variables by the two arms from the selected scheme
Design_result$baseline_table
# the cluster pair descriptive, which is useful for valid randomization check
Design_result$cluster_coin_des
# the overall allocation summary
Design_result$overall_allocations
## ----cvrallst1, fig.keep="all", fig.width = 7, fig.height=4---------------------------------------
# Stratification on location, with constrained randomization on other specified covariates
Design_stratified_result1 <- cvrall(clustername = Dickinson_design$county,
balancemetric = "l2",
x = data.frame(Dickinson_design[ , c("location", "inciis",
"uptodateonimmunizations",
"hispanic", "incomecat")]),
ntotal_cluster = 16,
ntrt_cluster = 8,
categorical = c("location", "incomecat"),
weights = c(1000, 1, 1, 1, 1),
cutoff = 0.1,
seed = 12345)
## ---- fig.keep="all", fig.width = 7, fig.height=4-------------------------------------------------
Design_stratified_result1$baseline_table
## ----cvrallst2, fig.keep="all", fig.width = 7, fig.height=4---------------------------------------
# An alternative and equivalent way to stratify on location
Design_stratified_result2 <- cvrall(clustername = Dickinson_design$county,
balancemetric = "l2",
x = data.frame(Dickinson_design[ , c("location", "inciis",
"uptodateonimmunizations",
"hispanic", "incomecat")]),
ntotal_cluster = 16,
ntrt_cluster = 8,
categorical = c("location", "incomecat"),
stratify = "location",
cutoff = 0.1,
seed = 12345,
check_validity = TRUE)
## ---- fig.keep="all", fig.width = 7, fig.height=4-------------------------------------------------
Design_stratified_result2$baseline_table
## ----cvrcov, fig.keep="all", fig.width = 7, fig.height=4------------------------------------------
# change the categorical variable of interest to have numeric representation
Dickinson_design_numeric <- Dickinson_design
Dickinson_design_numeric$location = (Dickinson_design$location == "Rural") * 1
Design_cov_result <- cvrcov(clustername = Dickinson_design_numeric$county,
x = data.frame(Dickinson_design_numeric[ , c("location", "inciis",
"uptodateonimmunizations",
"hispanic", "income")]),
ntotal_cluster = 16,
ntrt_cluster = 8,
constraints = c("s5", "mf.5", "any", "any", "mf0.4"),
categorical = c("location"),
###### Option to save the constrained space #####
# savedata = "dickinson_cov_constrained.csv",
seed = 12345,
check_validity = TRUE)
## ----set-options2, echo=FALSE, fig.keep="all", fig.width = 7, fig.height=4------------------------
options(width = 100)
## ---- fig.keep="all", fig.width = 7, fig.height=4-------------------------------------------------
# the allocation scheme from constrained randomization
Design_cov_result$allocation
# the statement about how many clusters to be randomized to the intervention and the control arms respectively
Design_cov_result$assignment_message
# the statement about how to get the whole randomization space to use in constrained randomization
Design_cov_result$scheme_message
# the data frame containing the allocation scheme, the clustername as well as the original data frame of covariates
Design_cov_result$data_CR
# the descriptive statistics for all the variables by the two arms from the selected scheme
Design_cov_result$baseline_table
# the cluster pair descriptive, which is useful for valid randomization check
Design_cov_result$cluster_coin_des
# the overall allocation summary
Design_cov_result$overall_allocations
## ---- echo=FALSE, results='asis'------------------------------------------------------------------
knitr::kable(head(Dickinson_outcome, 10))
## ----cptest, fig.keep="all", fig.width = 7, fig.height=4------------------------------------------
Analysis_result <- cptest(outcome = Dickinson_outcome$outcome,
clustername = Dickinson_outcome$county,
z = data.frame(Dickinson_outcome[ , c("location", "inciis",
"uptodateonimmunizations", "hispanic", "incomecat")]),
cspacedatname = system.file("dickinson_constrained.csv", package = "cvcrand"),
outcometype = "binary",
categorical = c("location","incomecat"))
## ----cptestre, fig.keep="all", fig.width = 7, fig.height=4----------------------------------------
Analysis_result
## ----info, results='markup', echo=FALSE-----------------------------------------------------------
sessionInfo()
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