Package 'PairedRand'

Create Matched Randomization

Description

The following function creates matched pair. With the matched pair, randomization is performed. SMD helps assess balance, but caution is required when interpreting results in small samples

Usage

create_matched_randomization(
  dat_in,
  group_label = c("SelfHelp", "Group"),
  seed = 177
)

Arguments

dat_in	Input dataset
group_label	The label for the two randomization group
seed	Seed for reproducibility

Value

A list containing:

dat_in_matched

A data frame containing participants organized into matched pairs based on similarity across baseline variables. The variable Pair identifies each matched pair.

dat_in_matched_rand

A data frame containing matched participants and their randomized study group assignments. This dataset is used for study implementation and assessment of balance between study groups.

dist_matrix

An object of class distancematrix from the nbpMatching package. The distance matrix quantifies similarity between participants and is used by the optimal nonbipartite matching algorithm to identify matched pairs. Smaller distances indicate greater similarity between participants.

Examples

dat_in <- data.frame(
  id = 1:4,
  age = c(25, 30, 35, 40),
  trait = c("A", "B", "A", "B")
)

results <- create_matched_randomization(dat_in, group_label = c("Treated", "Control"))
print(results$dat_in_matched_rand)

---

Data Simulation

Description

The following code generates a simulated dataset that will be used in running functions in the package

Usage

dat_sim(n_rows = 100, num_vars = 5, cat_vars = 3, cat_lev = 3, seed = 100)

Arguments

n_rows	Number of rows in the simulated dataset
num_vars	Number of numeric variables
cat_vars	Number of categorical variables
cat_lev	Number of levels for each categorical variable
seed	Seed for reproducibility

Value

A data frame containing simulated study data. The first column is a unique participant identifier (ID), followed by numeric variables (N_1, N_2, ...) and categorical variables (C_1, C_2, ...).

Examples

n_rows <- 100
num_vars <- 5
cat_vars <- 3
cat_lev <- 3
seed <- 100
dat_out <- dat_sim(n_rows, num_vars, cat_vars, cat_lev, seed)

---

Summary Statistics

Description

The following code generates summary statistics to describe balance across two arms Note: To save the output from summ_stats as an Excel file, refer to the as_hux_xlsx() function from the gtsummary package

Usage

summ_stats(dat_in, lab_list, arm = "randomization_group")

Arguments

dat_in	Dataset generated after randomization within pairs
lab_list	Variable labels for the dataset (can be set to NULL if not needed)
arm	Variable indicating the randomization group

Value

A gtsummary object of class tbl_strata containing descriptive statistics by study group, overall summary statistics, standardized mean differences (SMDs), and confidence intervals for assessing balance between randomized groups.

Examples

dat_in <- data.frame(
  id = 1:4,
  age = c(25, 30, 35, 40),
  trait = c("A", "B", "A", "B")
)

results <- create_matched_randomization(dat_in, group_label = c("Treated", "Control"))
results_matched_rand <- results$dat_in_matched_rand
dat_in <- results_matched_rand %>% dplyr::select(-c(Pair,ID))
results_matched__summary <- summ_stats(dat_in, lab_list = NULL, arm = "Group")

---