## ----setup, include=FALSE, message = FALSE, warning = FALSE-------------------
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
## ----echo=FALSE---------------------------------------------------------------
data <- data.frame("PK measure" = c("AUCinf ($\\mu$g*h/mL)","AUClast ($\\mu$g*h/mL)","Cmax ($\\mu$g/mL)"),
"SB2" = c("38,703 $\\pm$ 11,114", "36,862 $\\pm$ 9133", "127.0 $\\pm$ 16.9"),
"EU-INF" = c("39,360 $\\pm$ 12,332", "37,022 $\\pm$ 9398", "126.2 $\\pm$ 17.9"))
kableExtra::kable_styling(kableExtra::kable(data,
col.names = c("PK measure", "SB2", "Remicade (EU)"),
caption = "Primary PK measures between test and reference product. Data represent arithmetic mean +- standard deviation."),
bootstrap_options = "striped")
## -----------------------------------------------------------------------------
library(SimTOST)
# Sample size calculation for AUCinf
(sim_AUCinf <- sampleSize(
power = 0.9, # Target power
alpha = 0.05, # Significance level
arm_names = c("SB2", "EU_Remicade"), # Names of trial arms
list_comparator = list("EMA" = c("SB2","EU_Remicade")), # Comparator configuration
mu_list = list("SB2" = 38703, "EU_Remicade" = 39360), # Mean values
sigma_list = list("SB2" = 11114, "EU_Remicade" = 12332), # Standard deviation values
list_lequi.tol = list("EMA" = 0.80), # Lower equivalence margin
list_uequi.tol = list("EMA" = 1.25), # Upper equivalence margin
nsim = 1000 # Number of stochastic simulations
))
# Sample size calculation for AUClast
(sim_AUClast <- sampleSize(
power = 0.9, # Target power
alpha = 0.05, # Significance level
arm_names = c("SB2", "EU_Remicade"), # Names of trial arms
list_comparator = list("EMA" = c("SB2", "EU_Remicade")), # Comparator configuration
mu_list = list("SB2" = 36862, "EU_Remicade" = 37022), # Mean values
sigma_list = list("SB2" = 9133, "EU_Remicade" = 9398), # Standard deviation values
list_lequi.tol = list("EMA" = 0.80), # Lower equivalence margin
list_uequi.tol = list("EMA" = 1.25), # Upper equivalence margin
nsim = 1000 # Number of stochastic simulations
))
# Sample size calculation for Cmax
(sim_Cmax <- sampleSize(
power = 0.9, # Target power
alpha = 0.05, # Significance level
arm_names = c("SB2", "EU_Remicade"), # Names of trial arms
list_comparator = list("EMA" = c("SB2", "EU_Remicade")), # Comparator configuration
mu_list = list("SB2" = 127.0, "EU_Remicade" = 126.2), # Mean values
sigma_list = list("SB2" = 16.9, "EU_Remicade" = 17.9), # Standard deviation values
list_lequi.tol = list("EMA" = 0.80), # Lower equivalence margin
list_uequi.tol = list("EMA" = 1.25), # Upper equivalence margin
nsim = 1000 # Number of stochastic simulations
))
## -----------------------------------------------------------------------------
mu_list <- list(
SB2 = c(AUCinf = 38703, AUClast = 36862, Cmax = 127.0),
EUREF = c(AUCinf = 39360, AUClast = 37022, Cmax = 126.2)
)
sigma_list <- list(
SB2 = c(AUCinf = 11114, AUClast = 9133, Cmax = 16.9),
EUREF = c(AUCinf = 12332, AUClast = 9398, Cmax = 17.9)
)
## -----------------------------------------------------------------------------
list_comparator <- list("EMA" = c("SB2", "EUREF"))
list_lequi.tol <- list("EMA" = c(AUCinf = 0.8, AUClast = 0.8, Cmax = 0.8))
list_uequi.tol <- list("EMA" = c(AUCinf = 1.25, AUClast = 1.25, Cmax = 1.25))
## -----------------------------------------------------------------------------
(N_ss <- sampleSize(power = 0.9, # target power
alpha = 0.05,
mu_list = mu_list,
sigma_list = sigma_list,
list_comparator = list_comparator,
list_lequi.tol = list_lequi.tol,
list_uequi.tol = list_uequi.tol,
dtype = "parallel",
ctype = "ROM",
vareq = TRUE,
lognorm = TRUE,
nsim = 1000,
seed = 1234))
## -----------------------------------------------------------------------------
N_ss$response
## -----------------------------------------------------------------------------
(N_mult_corr <- sampleSize(power = 0.9, # target power
alpha = 0.05,
mu_list = mu_list,
sigma_list = sigma_list,
list_comparator = list_comparator,
list_lequi.tol = list_lequi.tol,
list_uequi.tol = list_uequi.tol,
rho = 0.6,
dtype = "parallel",
ctype = "ROM",
vareq = TRUE,
lognorm = TRUE,
nsim = 1000,
seed = 1234))
## -----------------------------------------------------------------------------
(N_mp_bon <- sampleSize(
power = 0.9, # Target power
alpha = 0.05, # Significance level
mu_list = mu_list, # List of means
sigma_list = sigma_list, # List of standard deviations
list_comparator = list_comparator, # Comparator configurations
list_lequi.tol = list_lequi.tol, # Lower equivalence boundaries
list_uequi.tol = list_uequi.tol, # Upper equivalence boundaries
rho = 0.6, # Correlation between endpoints
dtype = "parallel", # Trial design type
ctype = "ROM", # Test type (Ratio of Means)
vareq = TRUE, # Assume equal variances
lognorm = TRUE, # Log-normal distribution assumption
k = c("EMA" = 1), # Demonstrate equivalence for at least 1 endpoint
adjust = "bon", # Bonferroni adjustment method
nsim = 1000, # Number of stochastic simulations
seed = 1234 # Random seed for reproducibility
))
## -----------------------------------------------------------------------------
(N_mp_sid <- sampleSize(
power = 0.9, # Target power
alpha = 0.05, # Significance level
mu_list = mu_list, # List of means
sigma_list = sigma_list, # List of standard deviations
list_comparator = list_comparator, # Comparator configurations
list_lequi.tol = list_lequi.tol, # Lower equivalence boundaries
list_uequi.tol = list_uequi.tol, # Upper equivalence boundaries
rho = 0.6, # Correlation between endpoints
dtype = "parallel", # Trial design type
ctype = "ROM", # Test type (Ratio of Means)
vareq = TRUE, # Assume equal variances
lognorm = TRUE, # Log-normal distribution assumption
k = c("EMA" = 1), # Demonstrate equivalence for at least 1 endpoint
adjust = "sid", # Sidak adjustment method
nsim = 1000, # Number of stochastic simulations
seed = 1234 # Random seed for reproducibility
))
## -----------------------------------------------------------------------------
(N_mp_k <- sampleSize(
power = 0.9, # Target power
alpha = 0.05, # Significance level
mu_list = mu_list, # List of means
sigma_list = sigma_list, # List of standard deviations
list_comparator = list_comparator, # Comparator configurations
list_lequi.tol = list_lequi.tol, # Lower equivalence boundaries
list_uequi.tol = list_uequi.tol, # Upper equivalence boundaries
rho = 0.6, # Correlation between endpoints
dtype = "parallel", # Trial design type
ctype = "ROM", # Test type (Ratio of Means)
vareq = TRUE, # Assume equal variances
lognorm = TRUE, # Log-normal distribution assumption
k = c("EMA" = 2), # Demonstrate equivalence for at least 2 endpoints
adjust = "k", # Adjustment method
nsim = 1000, # Number of stochastic simulations
seed = 1234 # Random seed for reproducibility
))
## -----------------------------------------------------------------------------
(N_mp_seq <- sampleSize(
power = 0.9, # Target power
alpha = 0.05, # Significance level
mu_list = mu_list, # List of means
sigma_list = sigma_list, # List of standard deviations
list_comparator = list_comparator, # Comparator configurations
list_lequi.tol = list_lequi.tol, # Lower equivalence boundaries
list_uequi.tol = list_uequi.tol, # Upper equivalence boundaries
rho = 0.6, # Correlation between endpoints
dtype = "parallel", # Trial design type
ctype = "ROM", # Test type (Ratio of Means)
vareq = TRUE, # Assume equal variances
lognorm = TRUE, # Log-normal distribution assumption
adjust = "seq", # Sequential adjustment method
type_y = c("AUCinf" = 2, "AUClast" = 2, "Cmax" = 1), # Endpoint types
k = c("EMA" = 1), # Demonstrate equivalence for all 3 endpoints
nsim = 1000, # Number of stochastic simulations
seed = 1234 # Random seed for reproducibility
))