TIE for NTIDs [Power / Sample Size]

posted by d_labes  – Berlin, Germany, 2018-02-04 13:40 (2324 d 16:06 ago) – Posting: # 18340
Views: 20,512

Dear Astea, dear Helmut,

❝ ❝ I suspect TIE will be also inflated in studies of NTDs also (by using FDA approach or by using different confidence limits for two metrics).


❝ Might well be.


There is no reason to speculate. Fire up the all-in-one device suitable for every purpose PowerTOST :cool:
and use function power.NTIDFDA().

Try this (for the homoscedastic case swR=swT):
library(PowerTOST)
# number of subjects
n <- 12
design <- "2x2x4"
# theta is the reguatory constant for widening/shrinking the acceptance limits
# (see Warfarin guidance, but note: the theta there is theta^2)

theta <- -(log(0.9)/0.1)
# function to obtain the upper limit of the implied acceptance range
UL <- function(CV, theta){
  upper <- exp(theta* CV2se(CV))
  if (upper>1.25) upper <- 1.25
  upper
}

CVs <- seq(0.03, 0.4, by=0.01)
# reserve memory
GMR <- vector("numeric", length= length(CVs))
TIE <- vector("numeric", length= length(CVs))
ns  <- vector("numeric", length= length(CVs))

for (i in seq_along(CVs)){
  GMR[i] <- UL(CVs[i], theta)
  ns[i]  <- n
  # instead of a constant n we may use n obtained via sampleN.NTIDFDA()
  # then uncomment next line
  # ns[i]  <- sampleN.NTIDFDA(CV=CVs[i], print=FALSE, details=FALSE)[["Sample size"]]
  TIE[i] <- power.NTIDFDA(CV=CVs[i], theta0 = GMR[i], n=n, design=design, nsims=1E6)
}
print(data.frame(CV=CVs, GMR=GMR, n=ns, TIE=TIE), row.names=FALSE)


With n=12 we get:
   CV      GMR      TIE
 0.03 1.032106 0.049529
 0.04 1.043027 0.049529
 0.05 1.054058 0.049529
 0.06 1.065197 0.049529
 0.07 1.076443 0.049529
 0.08 1.087796 0.049529
 0.09 1.099256 0.049529
 0.10 1.110820 0.049529
 0.11 1.122489 0.049529
 0.12 1.134261 0.049529
 0.13 1.146135 0.049513
 0.14 1.158110 0.049401
 0.15 1.170186 0.048949
 0.16 1.182360 0.047774
 0.17 1.194633 0.045520
 0.18 1.207001 0.041949
 0.19 1.219466 0.037606
 0.20 1.232024 0.032688
 0.21 1.244675 0.027556
 0.22 1.250000 0.027744
 0.23 1.250000 0.031157
 0.24 1.250000 0.034106
 0.25 1.250000 0.036487
 0.26 1.250000 0.038232
 0.27 1.250000 0.039669
 0.28 1.250000 0.040685
 0.29 1.250000 0.041398
 0.30 1.250000 0.041859


But if we use n obtained to have a targetpower of 0.8, using theta0=0.975 we get:
 0.03 214 1.032106 0.051258
 0.04 54 1.043027 0.051405
 0.05 32 1.054058 0.051804
 0.06 24 1.065197 0.051418
 0.07 22 1.076443 0.051395
 0.08 20 1.087796 0.051232
 0.09 18 1.099256 0.051158
 0.10 18 1.110820 0.051158
 0.11 16 1.122489 0.050734
 0.12 16 1.134261 0.050734
 0.13 16 1.146135 0.050732
 0.14 16 1.158110 0.050705
 0.15 16 1.170186 0.050552
 0.16 16 1.182360 0.049877
 0.17 16 1.194633 0.048183
 0.18 16 1.207001 0.044852
 0.19 16 1.219466 0.040159
 0.20 16 1.232024 0.034550
 0.21 16 1.244675 0.028581
 0.22 16 1.250000 0.028871
 0.23 16 1.250000 0.033203
 0.24 16 1.250000 0.036778
 0.25 16 1.250000 0.039643
 0.26 16 1.250000 0.041844
 0.27 18 1.250000 0.044551
 0.28 18 1.250000 0.045817
 0.29 18 1.250000 0.046739
 0.30 20 1.250000 0.048178

Indeed a small alpha-inflation at low CVs'.
If we were Potvin-adepts this is a negligible alpha inflation, namely below 0.052.

Regards,

Detlew

Complete thread:

UA Flag
Activity
 Admin contact
23,057 posts in 4,840 threads, 1,641 registered users;
131 visitors (0 registered, 131 guests [including 81 identified bots]).
Forum time: 06:46 CEST (Europe/Vienna)

If you tell the truth you don’t have to remember anything.    Mark Twain

The Bioequivalence and Bioavailability Forum is hosted by
BEBAC Ing. Helmut Schütz
HTML5