update documentation & function names

NAMESPACE

@@ -3,8 +3,8 @@ importFrom(Rcpp, evalCpp)
 exportPattern("^[[:alpha:]]+")
 S3method(plot,simss)
 S3method(print,simss)
-export(N_Mielke)
-export(estSampleSize)
+export(sampleSize_Mielke)
+export(sampleSize)
 export(get_par)
 export(plot.simss)
 importFrom(data.table,.SD)

R/estSampleSize.R → R/SampleSize.R

@@ -6,8 +6,8 @@
 #' @param sigma_list  list of sigma vectors, each element corresponds to a comparator with a sigma vector of size number of endpoints.
 #' @param cor_mat matrix specifying the correlation matrix between endpoints, used along with sigma_list  to calculate the varcov list in case it is not provided.
 #' @param sigmaB number between subject variance only for 2x2 design.
-#' @param Eper  vector of size 2, effect of period on c(0,1).
-#' @param Eco vector of size 2, carry over effect of arm c(Reference, Treatment).
+#' @param Eper Vector of length 2, specifying the period effect in `dtype = "2x2"` design, applied to c(Period 0, Period 1).
+#' @param Eco Vector of length 2, specifying the carry-over effect for each arm in `dtype = "2x2"` design, applied to c(Reference, Treatment).
 #' @param rho Correlation parameter applied uniformly across all endpoint pairs, used with sigma_list to calculate varcov if cor_mat or varcov_list are not provided.
 #' @param TAR vector of allocation rates with allocation rates of the arm, default is equivalent rate.
 #' @param arm_names Optional vector with the treatment names. If not supplied, it will be derived from mu_list.
@@ -81,7 +81,7 @@
 #'                          FDA = c("AUClast", "Cmax"))
 #'
 #'# Run the simulation
-#'estSampleSize(power = 0.9, # target power
+#'sampleSize(power = 0.9, # target power
 #'              alpha = 0.05,
 #'              mu_list = mu_list,
 #'              sigma_list = sigma_list,
@@ -99,14 +99,8 @@
 #'              seed = 1234)
 #'
 #' @export
-estSampleSize <- function(mu_list,
-                    varcov_list=NA,
-                    sigma_list=NA,
-                    cor_mat=NA,
-                    sigmaB =NA,
-                    Eper = c(0,0),
-                    Eco = c(0,0),
-                    rho=0,
+sampleSize <- function(mu_list, varcov_list=NA, sigma_list=NA, cor_mat=NA,
+                       sigmaB =NA, Eper = c(0,0), Eco = c(0,0), rho=0,
                     TAR=NA,
                     arm_names=NA,
                     ynames_list=NA,
@@ -458,14 +452,20 @@ estSampleSize <- function(mu_list,
   }
 
 
-  if(dtype=="parallel"){
-    if(length(arm_names)!=length(dropout)){
-      warning("Incorrect number of dropout supplied (One needed for each arm),so it will be assigned a dropout=0")
+  if (dtype == "parallel") {
+    if (length(arm_names) != length(dropout)) {
+      warning("The number of dropout values provided does not match the number of arms specified in 'arm_names'. A default dropout rate of 0 will be assigned to each arm.")
       dropout <- rep(0,length(arm_names))
     }
-    if(is.null(names(dropout))){
-      names(dropout) <- arm_names}
-  }else{
+    if (is.null(names(dropout))) {
+      names(dropout) <- arm_names
+    }
+
+    # Check if dtype is "parallel" and Eper or Eco are non-default
+    if ((any(Eper != c(0, 0)) || any(Eco != c(0, 0)))) {
+      warning("Eper and Eco are only applicable for dtype = '2x2'. Non-default values for Eper or Eco will be ignored in parallel design.")
+    }
+  } else {
     if(length(dropout)!=2){
       warning("Incorrect number of dropout supplied (One needed for each sequence),so it will be assigned a dropout=0")
       dropout <- rep(0,2)

R/SimTOST-package.R

@@ -8,5 +8,5 @@
 #' @title Sample Size Estimation via Simulation
 #' @references
 #' Mielke, J., Jones, B., Jilma, B. & König, F. Sample Size for Multiple Hypothesis Testing in Biosimilar Development. \emph{Statistics in Biopharmaceutical Research} 10, 39–49 (2018).
-#' @seealso \link{estSampleSize}
+#' @seealso \link{sampleSize}
 "_PACKAGE"

R/get_par.R

@@ -1,5 +1,5 @@
 #' @title Parameter Configuration for Endpoints and Comparators
-#' @description Constructs and returns a list of key parameters (mean vectors, variance-covariance matrices, and allocation rates) required for input into the `estSampleSize` function. This function ensures that the parameters for each endpoint and comparator are consistent, properly named, and formatted.
+#' @description 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.
 #'
 #' @param mu_list A list of mean (\eqn{\mu}) vectors. Each element in the list represents a comparator, with the corresponding \eqn{\mu} vector having a length equal to the number of endpoints.
 #' @param varcov_list A list of variance-covariance matrices. Each element corresponds to a comparator, with a matrix of size \eqn{(n \times n)}, where \eqn{n} is the number of endpoints.

R/N_one_ep.r → R/sampleSize_Mielke.R

@@ -172,9 +172,18 @@ power_dom <- function(seed, mu_test, mu_control, sigma_test, sigma_control,
 #'   \item{"power.a"}{Achieved power with the estimated sample size.}
 #'   \item{"SS"}{Required sample size per sequence to achieve the target power.}
 #' }
+#'
+#' @examples
+#' # Example 1 from Mielke
+#' sampleSize_Mielke(power = 0.8, Nmax = 1000, m = 5, k = 5, rho = 0,
+#'                   sigma = 0.3, true.diff =  log(1.05), equi.tol = log(1.25),
+#'                   design = "parallel", alpha = 0.05, adjust = "no",
+#'                   seed = 1234, nsim = 100)
+#'
 #' @export
-N_Mielke <- function(power, Nmax, m, k, rho, sigma, true.diff, equi.tol,
-                     design, alpha, adjust="no", seed = NULL, nsim = 10000) {
+sampleSize_Mielke <- function(power, Nmax, m, k, rho, sigma, true.diff,
+                              equi.tol, design, alpha, adjust = "no",
+                              seed = NULL, nsim = 10000) {
 
   if (!is.null(seed)) {
     set.seed(seed)

README.Rmd

@@ -40,7 +40,7 @@ devtools::install_github("smartdata-analysis-and-statistics/SimTOST")
 
 ## Example
 
-The main features of this package is `estSampleSize` function which can be used to calculate sample size for individual and multiple endpoints. 
+The main features of this package is `sampleSize` function which can be used to calculate sample size for individual and multiple endpoints. 
 
 The example of using the functionality of this package can be found in these vignettes:
 

README.md

@@ -34,7 +34,7 @@ devtools::install_github("smartdata-analysis-and-statistics/SimTOST")
 
 ## Example
 
-The main features of this package is `estSampleSize` function which can be used
+The main features of this package is `sampleSize` function which can be used
 to calculate sample size for individual and multiple endpoints.
 
 The example of using the functionality of this package can be found in

tests/testthat/test-error_armnames.R

@@ -16,7 +16,7 @@
 #   # Same treatment allocation rate
 #   TAR = c(1,1,1) # we assume same allocation rate in both arms
 #
-#   expect_error(SimTOST::estSampleSize( mu_list = mu_list,
+#   expect_error(SimTOST::sampleSize( mu_list = mu_list,
 #                                     sigma_list = sigma_list,
 #                                     power = 0.9,
 #                                     dtype = "parallel",

tests/testthat/test-error_dif_mu_cov_tar.R

@@ -15,7 +15,7 @@ test_that("differences across lenght of mu, varcov and tar", {
   # Same treatment allocation rate
   TAR = c(1,1) # we assume same allocation rate in both arms
 
-  expect_error(SimTOST::estSampleSize( mu_list = mu_list,
+  expect_error(SimTOST::sampleSize( mu_list = mu_list,
                                       sigma_list = sigma_list,
                                       varcov_list = NA,
                                       TAR=TAR,

tests/testthat/test-error_length_mu_sd.R

@@ -16,7 +16,7 @@ test_that("error difference lenght of mu and sigma", {
   # Same treatment allocation rate
   TAR = c(1,1,1) # we assume same allocation rate in both arms
 
-  expect_error(SimTOST::estSampleSize( mu_list = mu_list,
+  expect_error(SimTOST::sampleSize( mu_list = mu_list,
                                       sigma_list = sigma_list,
                                       varcov_list = NA,
                                       power = 0.9,

tests/testthat/test-error_log_dom.R

@@ -15,7 +15,7 @@
 #   # Same treatment allocation rate
 #   TAR = c(1,1,1) # we assume same allocation rate in both arms
 #
-#   expect_error(SimTOST::estSampleSize( mu_list = mu_list,
+#   expect_error(SimTOST::sampleSize( mu_list = mu_list,
 #                                       sigma_list = sigma_list,
 #                                       varcov_list = NA,
 #                                       TAR=TAR,

tests/testthat/test-error_logf_rom_2x2.R

@@ -15,7 +15,7 @@
 #   # Same treatment allocation rate
 #   TAR = c(1,1,1) # we assume same allocation rate in both arms
 #
-#   expect_error(SimTOST::estSampleSize( mu_list = mu_list,
+#   expect_error(SimTOST::sampleSize( mu_list = mu_list,
 #                        sigma_list = sigma_list,
 #                        varcov_list = NA,
 #                        TAR=TAR,

tests/testthat/test-error_no_mu.R

@@ -12,7 +12,7 @@ test_that("error no mu_list provided", {
   # Same treatment allocation rate
   TAR = c(1,1,1) # we assume same allocation rate in both arms
 
-  expect_error(SimTOST::estSampleSize( mu_list = mu_list,
+  expect_error(SimTOST::sampleSize( mu_list = mu_list,
                                       sigma_list = sigma_list,
                                       varcov_list = NA,
                                       power = 0.9,