α and no ω [Power / Sample Size]

posted by Helmut Homepage – Vienna, Austria, 2018-02-03 00:39 (2326 d 05:11 ago) – Posting: # 18336
Views: 20,574

Hi Astea,

❝ I am suffering trying to understand the problems and perspectives of alpha-adjustment.


You are not alone. :crying:

❝ Wherever I look I see TIE inflation...

❝ For HVD:

❝   – FDA RSABE seemed to have problems for CV around 30%


Only ≤30% (see there).

❝ For well-known adaptive designs (Potvin, Xu...) TIE also behaves badly...


For most of them, not at all. Ones with a slight (!) inflation can be handled be more adjustment. Xu is fine.

❝ TIE may be also inflated by evaluating different methods for PK metrics (when one of them turns to be overpowered - as in the case of adaptive design or different CI for two Cmax and AUC).


Theoretically IUT should protect us. Benjamin and Detlew are working on a new function power.2TOST.sim() and updated sampleN.2TOST() in PowerTOST. As expected the overall TIE is much lower than the single ones.

❝ 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.

❝ Now you talk about higher-order design and dose-proportional studies...


That’s an endless story.

❝ So... Are there any ways to deal with it excepting the bright idea of iteratively adjusted alpha?


THX for calling it bright! For reference-scaling a new method* by the two Lászlós control the TIE much better than RSABE/ABEL – but only for full replicate designs. Yet another good reason to avoid the bloody partial replicate. Detlew already implemented a new function power.RSABE2L.sdsims() in PowerTOST. You need the development version on GitHub to run this code:
library(PowerTOST)
CV     <- 0.3
n      <- 24
design <- "2x2x4"
U.EMA  <- scABEL(CV, regulator="EMA")[["upper"]]
U.FDA  <- scABEL(CV, regulator="FDA")[["upper"]]
st     <- proc.time()[[3]]
TIE    <- data.frame(method=c("EMA ABEL", "EMA ABEL adj", "FDA RSABE",
                              "FDA RSABE adj", "2L FDA", "2L Hyslop",
                              "2L exact"),
                     TIE=rep(NA, 7), stringsAsFactors=FALSE)
res    <- scABEL.ad(CV=CV, theta0=U.EMA, n=n, design=design,
                    regulator="EMA", print=FALSE, sdsims=TRUE, nsims=1e6)
TIE[1, 2] <- res[["TIE.unadj"]]
TIE[2, 2] <- res[["TIE.adj"]]
res    <- scABEL.ad(CV=CV, theta0=U.FDA, n=n, design=design,
                    regulator="FDA", print=FALSE, nsims=1e6)
TIE[3, 2] <- res[["TIE.unadj"]]
TIE[4, 2] <- res[["TIE.adj"]]
if (as.character(packageVersion("PowerTOST")) >= "1.4.6.9000") {
  TIE[5, 2] <- power.RSABE2L.sds(CV=CV, theta0=U.FDA, n=n, design=design,
                                 SABE_test="hyslop", nsims=1e6)
  TIE[6, 2] <- power.RSABE2L.sds(CV=CV, theta0=U.FDA, n=n, design=design,
                                 SABE_test="fda", nsims=1e6)
  TIE[7, 2] <- power.RSABE2L.sds(CV=CV, theta0=U.FDA, n=n, design=design,
                                 SABE_test="exact", nsims=1e6)
}
rt <- proc.time()[[3]]-st
print(TIE, row.names=FALSE); cat("Runtime:", round(rt/60, 1), "minutes\n")

        method      TIE
      EMA ABEL 0.080639
  EMA ABEL adj 0.050000
     FDA RSABE 0.133510
 FDA RSABE adj 0.049999
        2L FDA 0.062536
     2L Hyslop 0.063513
      2L exact 0.066088
Runtime: 4.6 minutes


❝ While looking through the literature I've found only some suggestions of modificating ABE (with new procedures or nonlinear CI limits) or alternatives for ABE (like GSD or Two-stage designs)...



  1. Knahl SIE, Lang B, Fleischer F, Kieser M. A comparison of group sequential and fixed sample size designs for bioequivalence trials with highly variable drugs. Eur J Clin Pharmacol. 2018;74(5):549–59. doi:10.1007/s00228-018-2415-7.

  2. Molins E., Cobo E., Ocaña J. Two-stage designs versus European scaled average designs in bioequivalence studies for highly variable drugs: Which to choose? Stat Med. 2017;36(30):4777–4788. doi:10.1002/sim.7452.

One of the authors of [1] is user Ben in the Forum and a co-author of PowerTOST. Incidentally I reviewed both papers. Demanding but great fun at the end. :-D



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