Update vignettes and documentation

Author: tdebray123

R/SampleSize.R

@@ -71,22 +71,26 @@
 #' lequi.tol <- c(AUCinf = 0.8, AUClast = 0.8, Cmax = 0.8)
 #' uequi.tol <- c(AUCinf = 1.25, AUClast = 1.25, Cmax = 1.25)
 #'
-#' # arms to be compared
+#' # Arms to be compared
 #' list_comparator <- list(EMA = c("SB2", "EUREF"),
 #'                         FDA = c("SB2", "USREF"))
 #'
-#'# Endpoints to be compared
-#'list_y_comparator <- list(EMA = c("AUCinf", "Cmax"),
-#'                          FDA = c("AUClast", "Cmax"))
+#' # Endpoints to be compared
+#' list_y_comparator <- list(EMA = c("AUCinf", "Cmax"),
+#'                           FDA = c("AUClast", "Cmax"))
 #'
-#'# Run the simulation
-#'sampleSize(power = 0.9, alpha = 0.05, mu_list = mu_list,
-#'           sigma_list = sigma_list, lequi.tol = lequi.tol,
-#'           uequi.tol = uequi.tol, list_comparator = list_comparator,
-#'           list_y_comparator = list_y_comparator, adjust = "no",
-#'           dtype = "parallel", ctype = "ROM", vareq = FALSE,
-#'           lognorm = TRUE, ncores = 1, nsim = 50, seed = 1234)
+#' # Equivalence boundaries for each comparison
+#' lequi_lower <- c(AUCinf = 0.80, AUClast = 0.80, Cmax = 0.80)
+#' lequi_upper <- c(AUCinf = 1.25, AUClast = 1.25, Cmax = 1.25)
 #'
+#'# Run the simulation
+#' sampleSize(power = 0.9, alpha = 0.05, mu_list = mu_list,
+#'            sigma_list = sigma_list, list_comparator = list_comparator,
+#'            list_y_comparator = list_y_comparator,
+#'            list_lequi.tol = list("EMA" = lequi_lower, "FDA" = lequi_lower),
+#'            list_uequi.tol = list("EMA" = lequi_upper, "FDA" = lequi_upper),
+#'            adjust = "no", dtype = "parallel", ctype = "ROM", vareq = FALSE,
+#'            lognorm = TRUE, ncores = 1, nsim = 50, seed = 1234)
 #' @export
 sampleSize <- function(mu_list, varcov_list = NA, sigma_list = NA, cor_mat = NA,
                        sigmaB =NA, Eper, Eco, rho = 0, TAR = NULL,

man/sampleSize.Rd

@@ -20,8 +20,6 @@ knitr::opts_chunk$set(echo = TRUE)
 knitr::opts_chunk$set(comment = "#>", collapse = TRUE)
 options(rmarkdown.html_vignette.check_title = FALSE) #title of doc does not match vignette title
 doc.cache <- T #for cran; change to F
-
-library(dplyr)
 ```
 
 
@@ -43,7 +41,6 @@ sigma <- c(AUC = 0.25, Cmax = 0.3)
 lequi_lower <- c(AUC = log(0.80), Cmax = log(0.80))
 lequi_upper <- c(AUC = log(1.25), Cmax = log(1.25))
 
-
 ss <- sampleSize(power = 0.8, alpha = 0.05,
                  mu_list = list("R" = mu_r, "T" = mu_t),
                  sigma_list = list("R" = sigma, "T" = sigma),
@@ -55,7 +52,7 @@ ss <- sampleSize(power = 0.8, alpha = 0.05,
                  adjust = "no", ncores = 1, nsim = 10000, seed = 1234)
 ss
 ```
-The total sample size is `r ss$response %>% pull(n_total)` subjects.
+The total sample size is `r ss$response$n_total` subjects.
 
 
 

vignettes/sampleSize_crossover.Rmd

@@ -20,8 +20,6 @@ knitr::opts_chunk$set(echo = TRUE)
 knitr::opts_chunk$set(comment = "#>", collapse = TRUE)
 options(rmarkdown.html_vignette.check_title = FALSE) #title of doc does not match vignette title
 doc.cache <- T #for cran; change to F
-
-library(dplyr)
 ```
 
 
@@ -69,11 +67,14 @@ ss <- sampleSize(power = 0.8, alpha = 0.05,
                  adjust = "no", ncores = 1, nsim = 10000, seed = 1234)
 ss
 ```     
-For 80\% power, a total of `r ss$response %>% pull(n_total)` would be required. 
+For 80\% power, a total of `r ss$response$n_total` would be required. 
 
 
 # Multiple Correlated Co-Primary Endpoints
-In the second example, we have $k=m=5$, $\sigma = 0.3$ and $\rho = 0.8$. Again, we can estimate the sample size using the functions provided by @mielke_sample_2018: 
+In the second example, we have $k=m=5$, $\sigma = 0.3$ and $\rho = 0.8$. This example is also adapted from @mielke_sample_2018, who employed a difference-of-means test on the log scale. The sample size calculation can again be conducted using two approaches, both of which are illustrated below.
+
+## Approach 1: Using sampleSize_Mielke
+In the first approach, we calculate the required sample size for 80% power using the [sampleSize_Mielke()](../reference/sampleSize_Mielke.html) function. This method directly follows the approach described in @mielke_sample_2018, assuming a difference-of-means test on the log-transformed scale with specified parameters.
 
 ```{r, eval = TRUE}
 ssMielke <- sampleSize_Mielke(power = 0.8, Nmax = 1000, m = 5, k = 5, rho = 0.8, 
@@ -83,20 +84,25 @@ ssMielke <- sampleSize_Mielke(power = 0.8, Nmax = 1000, m = 5, k = 5, rho = 0.8,
                               nsim = 10000)
 ssMielke
 ```
-For 80\% power, `r ssMielke["SS"]` subjects per sequence (`r ssMielke["SS"] * 2` in total) would have been required. 
+For 80\% power, `r ssMielke["SS"]` subjects per sequence (`r ssMielke["SS"] * 2` in total) would be been required. 
 
-We can perform the same analysis using [sampleSize()](../reference/sampleSize.html). In this case, we provide estimates for $\mu$ and $\sigma$ on the original scale, assuming they follow a normal distribution on the log scale (`lognorm = TRUE`). Instead of testing the difference of log-transformed means, we now test the ratio of the (untransformed) means.
+## Approach 2: Using sampleSize
+Alternatively, the sample size calculation can be performed using the [sampleSize()](../reference/sampleSize.html) function. This method assumes that effect sizes are normally distributed on the log scale and uses a difference-of-means test (`ctype = "DOM"`) with user-specified values for `mu_list`, `sigma_list`, and the correlation `rho`. 
+
+```{r}
+mu_r <- c(AUC = log(1.00), Cmax = log(1.00))
+mu_t <- c(AUC = log(1.02), Cmax = log(1.03))
+sigma <- c(AUC = 0.25, Cmax = 0.3)
+lequi_lower <- c(AUC = log(0.80), Cmax = log(0.80))
+lequi_upper <- c(AUC = log(1.25), Cmax = log(1.25))
 
-```{r, eval = TRUE}
 ss <- sampleSize(power = 0.8, alpha = 0.05,
-                 mu_list = list("R" = rep(1.00, 5),
-                                "T" = rep(1.05, 5)),
-                 sigma_list = list("R" = rep(0.3, 5),
-                                   "T" = rep(0.3, 5)),
+                 mu_list = list("R" = mu_r, "T" = mu_t),
+                 sigma_list = list("R" = sigma, "T" = sigma),
                  rho = 0.8, # high correlation between the endpoints
-                 lequi.tol = rep(0.8, 5),
-                 uequi.tol = rep(1.25, 5),
-                 dtype = "parallel", ctype = "ROM", lognorm = TRUE, 
+                 list_lequi.tol = list("T_vs_R" = lequi_lower),
+                 list_uequi.tol = list("T_vs_R" = lequi_upper),
+                 dtype = "parallel", ctype = "DOM", lognorm = FALSE, 
                  adjust = "no", ncores = 1, k = 5, nsim = 10000, seed = 1234)
 ss
 ```