Parameter Configuration for Endpoints and Comparators

Constructs and returns a list of key parameters (mean vectors, variance-covariance matrices, and allocation rates) required for input into the sampleSize function. This function ensures that the parameters for each endpoint and comparator are consistent, properly named, and formatted.

get_par(
  mu_list,
  varcov_list,
  TAR_list,
  type_y = NA,
  arm_names = NA,
  y_names = NA
)

Arguments

mu_list: A list of mean (\(\mu\)) vectors. Each element in the list represents a comparator, with the corresponding \(\mu\) vector having a length equal to the number of endpoints.
varcov_list: A list of variance-covariance matrices. Each element corresponds to a comparator, with a matrix of size \((n \times n)\), where \(n\) is the number of endpoints.
TAR_list: A list of treatment allocation rates (TARs) for each comparator. Each element contains a numeric value (can be fractional or integer) representing the allocation rate for the respective comparator.
type_y: A numeric vector specifying the type of each endpoint. Use 1 for primary endpoints and 2 for secondary or other endpoints.
arm_names: (Optional) A character vector containing names of the arms. If not provided, default names (e.g., T1, T2, ...) will be generated.
y_names: (Optional) A character vector containing names of the endpoints. If not provided, default names (e.g., y1, y2, ...) will be generated.

Value

A named list with the following components:

mu: A list of mean vectors, named according to arm_names.
varcov: A list of variance-covariance matrices, named according to arm_names.
tar: A list of treatment allocation rates (TARs), named according to arm_names.
type_y: A vector specifying the type of each endpoint.
weight_seq: A weight sequence calculated from type_y, used for endpoint weighting.
y_names: A vector of names for the endpoints, named as per y_names.

#' @details This function ensures that all input parameters (mu_list, varcov_list, and TAR_list) are consistent across comparators and endpoints. It performs checks for positive semi-definiteness of variance-covariance matrices and automatically assigns default names for arms and endpoints if not provided.

Examples

mu_list <- list(c(0.1, 0.2), c(0.15, 0.25))
varcov_list <- list(matrix(c(1, 0.5, 0.5, 1), ncol = 2), matrix(c(1, 0.3, 0.3, 1), ncol = 2))
TAR_list <- list(0.5, 0.5)
get_par(mu_list, varcov_list, TAR_list, type_y = c(1, 2), arm_names = c("Arm1", "Arm2"))
#> $mu
#> $mu$Arm1
#> [1] 0.1 0.2
#> 
#> $mu$Arm2
#> [1] 0.15 0.25
#> 
#> 
#> $varcov
#> $varcov$Arm1
#>      [,1] [,2]
#> [1,]  1.0  0.5
#> [2,]  0.5  1.0
#> 
#> $varcov$Arm2
#>      [,1] [,2]
#> [1,]  1.0  0.3
#> [2,]  0.3  1.0
#> 
#> 
#> $tar
#> $tar$Arm1
#> [1] 0.5
#> 
#> $tar$Arm2
#> [1] 0.5
#> 
#> 
#> $type_y
#> [1] 1 2
#> 
#> $weight_seq
#> [1] 1 1
#> 
#> $y_names
#> [1] "y1" "y2"
#>