Bioequivalence and Bioavailability Forum

❝ ❝ In coding the function sampleN.scABEL.ad() I tried to be more specific (see also my post below) and it gives for \(\small{CV_\textrm{wT}=CV_\textrm{wR}=0.3532}\):

Regulatory settings: EMA (ABEL)


❝ ❝ Expanded limits    : 0.7706 ... 1.2977


❝ ❝ Upper scaling cap  : CVwR > 0.5

❝ The Expanded limits are only valid if the CV's are exactly the assumed ones.

❝ They don't play any role during the simulations because the expansion applied depends on the CVwR of actual study, gradually different from study to study of the simulation.

❝ That was the reason why the ugly programmer of the function sampleN.scABEL() abstain from giving that numbers .

❝ But my point for Bebac user was- He/She shall enhance the limit based on Swr and then calculate the subject size. I am still a fan of Dave's FARTSSIE and uses older version.

• The partial replicate design is not supported – though I don’t miss it.
  
• The sample size is estimated for the 2×2×2 design and then adjusted according to the selected design (⅔ for the 3-period full replicate and ½ for the 4-period full replicate).
  
• But:
  • In ABEL the limits are not fixed like in ABE but depend on the (observed, realized) \(\small{\widehat{s}_\textrm{wR}}\) instead of an assumed one. You can only assume a \(\small{\widehat{\sigma}_\textrm{wR}}\) and simulate a reasonable large number of studies with different ones.
    Hint: \(\small{\widehat{s}_\textrm{wR}^2}\) follows a \(\small{\chi^2\textsf{-}}\)dis­tri­bution with \(\small{n-2}\) degrees of freedom and \(\small{\theta_0}\) follows a lognormal distribution.
    
  • Additionally you have to take the upper cap of scaling (50% for the EMA and ≈57.4% for Health Canada) and the PE constraint (within 80.00–125.00%) into account.
    

library(PowerTOST)
balance <- function(n, seq) {
  return(as.integer(seq * (n %/% seq + as.logical(n %% seq))))
}
sampleN.FARTSSIE <- function(CV, theta0, targetpower, design) {
  admin   <- as.integer(substr(design, 1, 1)) *
             as.integer(substr(design, 3, 3)) *
             as.integer(substr(design, 5, 5))
  LU      <- setNames(scABEL(CV), c("theta1", "theta2"))
  n.cross <- sampleN.TOST(CV = CV, theta0 = theta0,
                          theta1 = LU[["theta1"]],
                          theta2 = LU[["theta2"]],
                          targetpower = targetpower,
                          design = "2x2x2",
                          method = "noncentral",
                          print = FALSE)[["Sample size"]]
  # Note: FARTSSIE leaves rounding up to
  #       balanced sequences to the user
  return(balance(n.cross * 8 / admin,
                 as.integer(substr(design, 3, 3))))
}
theta0      <- 0.90
targetpower <- 0.80
CV          <- seq(0.3, 0.5, 0.05)
# The partial replicate design (TRR|RTR|RRT) is not supported in FARTSSIE
# and Balaam’s design (TR|RT|TT|RR) is not supported in sampleN.scABEL().
res         <- data.frame(CV = CV, design = rep(c("2x2x3", "2x2x4"),
                                                each = length(CV)),
                          n.FARTSSIE = NA_integer_, pwr.FARTSSIE = NA_real_,
                          n.scABEL = NA_integer_, pwr.scABEL = NA_real_,
                          pwr.SABE = NA_real_, pwr.PE = NA_real_,
                          pwr.ABE = NA_real_)
for (j in 1:nrow(res)) {
  res$n.FARTSSIE[j]   <- sampleN.FARTSSIE(CV = res$CV[j], theta0 = theta0,
                                          targetpower = targetpower,
                                          design = res$design[j])
  res$pwr.FARTSSIE[j] <- power.scABEL(CV = res$CV[j], theta0 = theta0,
                                      n = res$n.FARTSSIE[j], design = res$design[j])
  tmp                 <- sampleN.scABEL(CV = res$CV[j], theta0 = theta0,
                                        targetpower = targetpower,
                                        design = res$design[j], details = FALSE,
                                        print = FALSE)
  res$n.scABEL[j]     <- tmp[["Sample size"]]
  res$pwr.scABEL[j]   <- tmp[["Achieved power"]]
  # retrieve components: pure SABE, PE within constraints, ABE
  res[j, 7:9]         <- suppressMessages(
                           power.scABEL(CV = res$CV[j], theta0 = theta0,
                                        design =res$design[j],
                                        n = res$n.scABEL[j],
                                        details = TRUE)[2:4])
}
names(res)[c(4, 6)] <- "power"
res[, 4:9] <- signif(res[, 4:9], 4)
print(res, row.names = FALSE)

   CV design n.FARTSSIE  power n.scABEL  power pwr.SABE pwr.PE pwr.ABE
 0.30  2x2x3         54 0.8242       50 0.8016   0.8016 0.9900  0.7450
 0.35  2x2x3         44 0.7634       50 0.8037   0.8039 0.9776  0.6351
 0.40  2x2x3         36 0.7246       46 0.8073   0.8086 0.9548  0.5164
 0.45  2x2x3         34 0.7198       42 0.8002   0.8006 0.9266  0.4127
 0.50  2x2x3         32 0.6880       42 0.8035   0.8035 0.9065  0.3484
 0.30  2x2x4         40 0.8525       34 0.8028   0.8028 0.9906  0.7517
 0.35  2x2x4         32 0.7929       34 0.8118   0.8119 0.9786  0.6424
 0.40  2x2x4         28 0.7829       30 0.8066   0.8074 0.9528  0.5069
 0.45  2x2x4         26 0.7846       28 0.8112   0.8116 0.9266  0.4107
 0.50  2x2x4         24 0.7536       28 0.8143   0.8143 0.9065  0.3476

sampleN.scABEL(CV = 0.35, design = "2x2x3", regulator = "GCC", details = FALSE)

+++++++++++ scaled (widened) ABEL +++++++++++
            Sample size estimation
   (simulation based on ANOVA evaluation)
---------------------------------------------
Study design: 2x2x3 (3 period full replicate)
log-transformed data (multiplicative model)
1e+05 studies for each step simulated.

alpha  = 0.05, target power = 0.8
CVw(T) = 0.35; CVw(R) = 0.35
True ratio = 0.9
ABE limits / PE constraint = 0.8 ... 1.25
Widened limits = 0.75 ... 1.333333
Regulatory settings: GCC

Sample size
 n     power
42   0.8139

sampleN.TOST(CV = 0.35, theta0 = 0.9, theta1 = 0.75, design = "2x2x3")

+++++++++++ Equivalence test - TOST +++++++++++
            Sample size estimation
-----------------------------------------------
Study design: 2x2x3 (3 period full replicate)
log-transformed data (multiplicative model)

alpha = 0.05, target power = 0.8
BE margins = 0.75 ... 1.333333
True ratio = 0.9,  CV = 0.35

Sample size (total)
 n     power
34   0.811011

❝ On the other part, if the assumed PE is 0.9126-1.096, subject size of 40 could still be good considering no dropouts (Using Boss button).

1. Executive Board of the Health Ministers’ Council for GCC States. The GCC Guidelines for Bio­equi­va­lence. May 2021. Online.
   
2. League of Arab States, Higher Technical Committee for Arab Pharmaceutical Industry. Harmonised Arab Guide­line on Bioequivalence of Generic Pharmaceutical Products. Cairo. March 2014. Online.
   
3. Medicines Control Council. Registration of Medicines: Biostudies. Pretoria. June 2015. Online.