## ----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()