Bioequivalence and Bioavailability Forum • Deep shit

Deep shit [RSABE / ABEL]

posted by Helmut – Vienna, Austria, 2020-01-31 15:00 (1809 d 08:26 ago) – Posting: # 21119
Views: 25,509

Hi Mikalai,

❝ You seem to strengthen my doubts.

Fine.

❝ ❝ If you fail ABE (first assessment with a nominal α 0.05), you assess it for ABEL (second assessment with a nominal α 0.05). […] Two tests, each performed at level 0.05. Inflated Type I Error. Full stop.

❝ It has no practical sense for me. As I know trials frequently recalculated with some modifications, usually slight ones. I saw that the FDA did this, for example. I do not see that alpha levels were reduced in those recalculations.

Not limited to the FDA. We (CROs, applicants) could never, ever recalculate a study because that would be judged by assessors as cherry-picking. :cherry picking:

Of course, regulators play not in another league but in another sport, which can be expressed as

Quod licet Iovi, non licet bovi

The problem with a recalculation is – since the entire α was already spent in the original analysis – the TIE → ∞. Now what? If a passing study fails now, no problem; the risk is only a theoretical one since the product will not be marketed based on this study. Question: What is the TIE if the recalculated study passes as well?

❝ If we employ this approach (no recalculations or lower alpha level) …

Once you started with an α which controls the TIE in the original analysis, any lower α in the recalculation cannot work. OK, you could submit all studies with a 92% CI (α 0.04) and – if a recalculation is requested – perform it with a 98% CI (α 0.01). Power drops through the floor, good luck.

❝ … we may paralyze the whole industry …

Agree, also with your examples.

❝ Actually, we can ask people from the forum how many sponsors or CROs they know whose trials have never been recalculated?

Good idea. Only very few of mine. ;-)

❝ It seems that we have to multiple the ABE column by 2 to get full the sample size but not the ABEL column; otherwise, it has no much sense for me.

Duno what you mean. Are you considering the sample size of a 2×2×2 crossover? The sample size of the 2-period 4-sequence full replicate is ~½ though the number of treatments / biosamples (driving the study cost) are essentially the same.

CV (%) ABE.2x2x2 ABE.2x2x4 n.ABEL n.ABEL.Bonf n.ABEL.adj n.Molins

    20        20        10     18          24         18       22

    30        40        20     34          44         42       42

    40        66        34     30          38         32       36

    50        98        50     28          34         28       32

❝ I also wonder if the dropout rate has been considered in the calculations.

No. That’s specific to the drug and I never “compensate for potential dropouts in order to maintain power” unless I expect a dropout rate of >15%. Waste of money because the impact of dropouts on power is generally small. Try the functions pa.ABE() and pa.scABE() of PowerTOST.

❝ Looking at your table and slides post, it is appears that in the region CV between 30 - 40% the sample size for ABEL and ABE trials may be very close to each other.

The plots in the slide are all for ABEL. A comparison of ABE and the EMA’s (unadjusted) ABEL:

[image]

At 30% sample sizes are 40 and 34, at 40% 68 and 30.

❝ If it is true, the ABEL trials (CV between 30-40) should not be allowed by ethic and regulatory bodies because of unnecessary risks for subjects. It invalidates the RABE/ABEL approach in this region.

Hhm, not sure what you mean.

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Helmut Schütz

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Complete thread:

Statistical evaluation and BE hypotheses in full replicate design Elena777 2020-01-28 07:02 [RSABE / ABEL]
- Inflation of the TIE as well Helmut 2020-01-29 15:38
  - Inflation of the TIE as well Elena777 2020-01-29 20:01
    - Tricky… Helmut 2020-01-30 12:07
      - Tricky… Mikalai 2020-01-30 13:08
        
        Terrible… Helmut 2020-01-30 15:09
        
        Tricky… Mikalai 2020-01-30 16:02
        
        Deep shit Helmut 2020-01-30 16:59
        
        Deep shit Mikalai 2020-01-30 18:39
        
        Deep shit Mikalai 2020-01-30 19:31
        Deep shit Helmut 2020-01-30 20:07
        
        Deep shit Mikalai 2020-01-31 11:14
        
        Deep shitHelmut 2020-01-31 14:00
        
        Deep shit Mikalai 2020-01-31 16:07
        
        Flawed evaluation accepted Helmut 2020-01-31 12:19
        
        Flawed evaluation accepted Mikalai 2020-01-31 12:40
        
        Flawed evaluation accepted Helmut 2020-01-31 14:17
        
        Flawed evaluation accepted Mikalai 2020-01-31 16:41
        
        Flawed approach even if accepted ? Helmut 2020-01-31 20:28
        
        Flawed approach even if accepted ? Mikalai 2020-02-01 16:18
        
        misunderstanding mittyri 2020-02-01 21:34
        
        misunderstanding Mikalai 2020-02-06 13:41
        
        misunderstanding mittyri 2020-02-06 16:23
        
        The globe is flat! d_labes 2020-02-05 19:16
      - Tricky… wienui 2020-01-30 18:53
        
        Tricky… Helmut 2020-01-30 19:18
        
        Tricky… wienui 2020-02-03 07:10
        
        ABE vs. ABEL Helmut 2020-02-03 12:25
        
        zigzag d_labes 2020-02-05 18:53
        
        zigzag Helmut 2020-02-05 19:46
        
        zigzag Mikalai 2020-02-06 11:38
        
        helter-skelter Helmut 2020-02-06 20:12
        
        helter-skelter Mikalai 2020-02-10 16:10
  - Inflation of the TIE as well zizou 2020-02-01 17:00
    - Inflation of the TIE as well nobody 2020-02-01 23:30
  - Inflation of the TIE as well Elena777 2020-03-10 19:28
    - Fishing in the dark Helmut 2020-03-10 21:06
- Statistical evaluation and BE hypotheses in full replicate design nobody 2020-02-03 15:07
  - TIE, repeat once more please... Astea 2020-04-02 12:41