Bioequivalence and Bioavailability Forum • α and no ω

α and no ω [Power / Sample Size]

posted by Helmut – Vienna, Austria, 2018-02-03 00:39 (2267 d 09:55 ago) – Posting: # 18336
Views: 20,110

Hi Astea,

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

You are not alone.

❝ 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)...

❝

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.
❝
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

Tóthfalusi L, Endrényi L. Algorithms for evaluating reference scaled average bioequivalence: Power, bias, and consumer risk. Stat Med. 2017;36(27):4378–90. doi:10.1002/sim.7440.

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Helmut Schütz

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Complete thread:

Sample size for 4-period 4-sequence crossover BE study Bryony Simmons 2018-02-01 12:16 [Power / Sample Size]
- function sampleN.TOST of package PowerTOST Helmut 2018-02-01 13:01
  - Alpha adjustment in higher order crossover d_labes 2018-02-01 14:01
    - Deficiencies Helmut 2018-02-01 15:48
      - Deficiencies nobody 2018-02-01 17:10
      - Deficiencies d_labes 2018-02-01 18:57
        
        Deficiencies Relaxation 2018-02-02 11:12
        
        Deficiencies nobody 2018-02-02 12:49
        Deficiencies Helmut 2018-02-02 16:14
        
        Deficiencies Relaxation 2018-02-02 19:41
        
        alpha... where is omega? Astea 2018-02-02 21:45
        
        α and no ωHelmut 2018-02-02 23:39
        
        TIE for NTIDs d_labes 2018-02-04 12:40
        
        TIE for NTIDs Astea 2018-02-04 20:04
        
        TIE for NTIDs Helmut 2018-02-05 01:01
        
        TIE for NTIDs d_labes 2018-02-05 16:40
        
        TIE for NTIDs Helmut 2018-02-05 17:49
        
        TIE for NTIDs d_labes 2018-02-05 22:17
        
        TIE for NTIDs Helmut 2018-02-06 12:34
        
        TIE for NTIDs d_labes 2018-02-05 16:35
        
        bow TIE for NTIDs Astea 2018-02-05 17:52
        
        bow TIE for NTIDs Helmut 2018-02-05 18:10
        
        111.11 for NTIDs Astea 2018-02-05 19:27
        
        111.11 for NTIDs Helmut 2018-02-06 00:12
        
        bow TIE for NTIDs d_labes 2018-02-05 22:33