Sereng
☆

USA,
2022-01-31 00:11
(414 d 09:06 ago)

Posting: # 22761
Views: 1,385

## Sample Size "Effi­ci­ency" of Replicate CO vs. 2-way CO for NTID [Power / Sample Size]

Dear colleagues, I am struggling with understanding the sample size “benefits” of a replicate study design for NTID. Specifically, for levothyroxine (T4), FDA states that the RSABE approach is to properly adjust the BE criteria based on reference variability and comparing T and R product within-subject variability. The T4, the OGD PSG (cross-referenced to the Warfarin PSG) states that the study must be replicate (2x2x4) and BE established using Cmax and AUC if: (1) Upper 95% CI is ≤0 using RSABE; (2) The 90% CI is within 80-125% (ABE); and (3) The upper limit of the 90% equal-tails CI of the within subject SD of T to R is less than 2.5

What are the sample size “benefits” or “efficiencies” of undertaking this study as a replicate CO design (e.g., Test 50 x 2; Ref 50 x 2) versus a 2-way CO (Test 100 x 2; Ref 100 x 2). Any help would be much appreciated. Many thanks!
ElMaestro
★★★

Denmark,
2022-01-31 00:50
(414 d 08:27 ago)

@ Sereng
Posting: # 22762
Views: 1,149

## Sample Size "Efficiency" of Replicate CO vs. 2-way CO for NTID

Hi Sereng,

❝ What are the sample size “benefits” or “efficiencies” of undertaking this study as a replicate CO design (e.g., Test 50 x 2; Ref 50 x 2) versus a 2-way CO (Test 100 x 2; Ref 100 x 2). Any help would be much appreciated.

I completely understand your question, but I think it is slightly the wrong question to ask.
If you look at it from the regulator's side, they have had some pretty nasty experiences with Levo through the years and it may have gone so pear-shaped that regulators as a precautionary measure had to use some degree of belt-and-suspenders thinking and where the focus was on de-risking anything related to the patient. Hence additional requirements, like some degree of similarity of T and R variabilities.

We started out with the good old work horse, the 222BE design with plain average BE. Great for many things.
But "hey, poor me", said the Sponsor, "now I need 326 subjects in my trial for Schützoycin tablets. I demand an easier approach!".
Then regulators meditated 10 years and held conference all the while and at some point it looked like average BE was being scrapped and fancy things like Pop BE and Individual BE were winning the crowd. But when the smoke had cleared average BE was still standing, this time in combination with replicate designs. PopBE and Individual BE were soon on their way out. Widened limits in different flavours saw daylight. Sample sizes for HVDPs got normal (and some people began bashing others on their heads with guidance collections, pharmacopeias and wooden clubs for confusing HVDPs and HVDs, but that's a story for another day).
Then, fueled by a grieving innovator industry, who realised that revenue was lost on the HVDPs, attention was suddenly drawn to NTIs. Claims of doom and gloom lurking around every corner for generics of those products. FDA dismissed the matter: "If we can widen the limits for HVDP's then we can just put the selector in reverse and step on the pedal for NTIs". The innovators lobbyed hard. "Dose dumping this, levothyroxine that, internal bleeding here, Hancock-Mortimer syndrome there, look what happened in Europe, and do you really want to murder the average epileptic patient when she is switched from innovator to generic just because the guidance overlooks an obvious problem?"
And so FDA for fear of lawsuits and further pressure from patient organisation went: "OK, erm... right... we'll slap on some additional requirements to make even more sure that T and R behave the same when we talk NTIs." Kind of disappointing to the generic industry (and possibly also select members of the legal profession).
And there you have it. I wonder what will come next.

Pass or fail!
ElMaestro
d_labes
★★★

Berlin, Germany,
2022-01-31 19:48
(413 d 13:28 ago)

@ ElMaestro
Posting: # 22764
Views: 1,071

## OT

Dear Anders,

❝ ...

❝ We started out with the good old work horse, the 222BE design with plain average BE. Great for many things.

❝ But "hey, poor me", said the Sponsor, "now I need 326 subjects in my trial for Schützoycin tablets. I demand an easier approach!".

❝ Then regulators meditated 10 years and held conference all the while and at some point it looked like average BE was being scrapped and fancy things like Pop BE and Individual BE were winning the crowd. But when the smoke had cleared average BE was still standing ...

what a sparkling gem of literature! Kudos

I highly recommend you to change your profession to be a dime novelist or a ghost writer of love letters for scientists, especially such working on the field of bioequivalence or similar nonsens

Regards,

Detlew
Helmut
★★★

Vienna, Austria,
2022-01-31 00:52
(414 d 08:25 ago)

@ Sereng
Posting: # 22763
Views: 1,166

## Follow the guidance (if possible)

Hi Sereng,

❝ What are the sample size “benefits” or “efficiencies” of undertaking this study as a replicate CO design (e.g., Test 50 x 2; Ref 50 x 2) versus a 2-way CO (Test 100 x 2; Ref 100 x 2). Any help would be much appreciated.

None. In both cases you risk a Refuse-to-Receive because you deviate from the guidance (recommending the 20 µg strength administered as a 60 µg dose). Theoretically you could deviate from the guidance if you have good reasons for doing so and initiate a Controlled Correspondence. However, I think your chance is close to nil because the guidance offers the option of biowaivers for the strengths 13, 25, 50, 75, 88, 100, 112, 125, 137, 150, and 175 µg. Only if you can’t perform a study with the 20 µg strength – because you don’t have in your line of strengths – you can give it a try.

Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮
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Sereng
☆

USA,
2022-01-31 22:58
(413 d 10:18 ago)

@ Helmut
Posting: # 22765
Views: 1,210

## Follow the guidance (if possible)

❝ ❝ What are the sample size “benefits” or “efficiencies” of undertaking this study as a replicate CO design (e.g., Test 50 x 2; Ref 50 x 2) versus a 2-way CO (Test 100 x 2; Ref 100 x 2). Any help would be much appreciated.

❝ None. In both cases you risk a Refuse-to-Receive because you deviate from the guidance (recommending the 20 µg strength administered as a 60 µg dose). Theoretically you could deviate from the guidance if you have good reasons for doing so and initiate a Controlled Correspondence. However, I think your chance is close to nil because the guidance offers the option of biowaivers for the strengths 13, 25, 50, 75, 88, 100, 112, 125, 137, 150, and 175 µg. Only if you can’t perform a study with the 20 µg strength – because you don’t have in your line of strengths – you can give it a try.

Hi Helmut

Many thanks for your response. This is my first time responding on this forum. I am not even sure if I need to reply before or after your text. Is it possible you misunderstood my question? What I meant to ask is if I do replicate crossover, i.e., 50 subjects on Test x 2 periods and 50 subjects on Reference x 2 periods (replicate crossover, 2X2X4) as opposed to 100 subjects on test x 1 period and 100 subjects on Reference x 1 period (2X2X2), do I gain any sample size (or power) efficiency using FDA 3-tests (per PSG) for Levothyroxine? Many thanks!

Biostatistically Challenged CEO
Helmut
★★★

Vienna, Austria,
2022-02-01 00:53
(413 d 08:23 ago)

@ Sereng
Posting: # 22766
Views: 1,096

## Now I got it!

Hi Sereng,

❝ Many thanks for your response.

Welcome – though I missed the target.

❝ I am not even sure if I need to reply before or after your text.

See there.

❝ Is it possible you misunderstood my question?

Given what you posted in the following, yes, indeed.

❝ What I meant to ask is if I do replicate crossover, i.e., 50 subjects on Test x 2 periods and 50 subjects on Reference x 2 periods (replicate crossover, 2X2X4) as opposed to 100 subjects on test x 1 period and 100 subjects on Reference x 1 period (2X2X2), do I gain any sample size (or power) efficiency using FDA 3-tests (per PSG) for Levothyroxine?

That’s hypothetical cause the FDA will not accept a 2×2×2 crossover. Study cost hinges mainly on the number of treatments (which drives the number of samples and hence, costs of bioanalytics). Peanuts: In a replicate (less subjects) you safe some costs of pre-/post study lab exams which might be outweighed by a higher chance of dropouts.
See Fig.1.
Anyway: Let’s compare the FDA’s RSABE and the EMA’s fixed limits of 90.00–111.11% (in 2×2×2 and 2×2×4 crossovers) to conventional ABE with fixed limits of 80.00–125.00% (2×2×2 crossover)* based on data assessed by the FDA in 2011.

library(PowerTOST) # Yu (2011) https://www.fda.gov/media/82940/Download # 9 ANDAs of Levothyroxine: Cmax CV     <- c(0.052, 0.096, 0.186) # min, mean, max) theta0 <- 0.975                  # assumed T/R-ratio target <- 0.80                   # target power ≥80% x      <- data.frame(CV = CV, n.FDA = NA_integer_, cost.FDA = NA_real_,                      n.EMA2 = NA_integer_, cost.EMA2 = NA_real_,                      n.EMA4 = NA_integer_, cost.EMA4 = NA_real_,                      n.ABE = NA_integer_, cost.ABE = 1) for (j in seq_along(CV)) {   # RSABE for NTIDs, 2x2x4 design mandatory acc. to the guidance   x$n.FDA[j] <- sampleN.NTIDFDA(CV = CV[j], theta0 = theta0, targetpower = target, details = FALSE, print = FALSE)[["Sample size"]] # EMA for NTIDs, fixed limits 90.00-111.11% # 2x2x2 design (in product-specific guidance for NTIDs) x$n.EMA2[j] <- sampleN.TOST(CV = CV[j], theta0 = theta0, theta1 = 0.90,                               targetpower = target, design = "2x2x2",                               print = FALSE)[["Sample size"]]   # 2x2x4 design (optional)   x$n.EMA4[j] <- sampleN.TOST(CV = CV[j], theta0 = theta0, theta1 = 0.90, targetpower = target, design = "2x2x4", print = FALSE)[["Sample size"]] # conventional ABE, 2x2x2 design, fixed limits 80.00-125.00% x$n.ABE[j] <- sampleN.TOST(CV = CV[j], theta0 = theta0, theta1 = 0.80,                           targetpower = target, design = "2x2x2",                           print = FALSE)[["Sample size"]]   # minimum sample size acc. to the guideline   if (x$n.EMA2[j] < 12) x$n.EMA2[j] <- 12   if (x$n.EMA4[j] < 12) x$n.EMA4[j] <- 12   if (x$n.ABE[j] < 12) x$n.ABE[j] <- 12 } # cost relative to ABE 2×2×2 design with fixed limits 80.00-125.00% x$cost.FDA <- x$n.FDA * 2 / x$n.ABE x$cost.EMA2 <- x$n.EMA2 / x$n.ABE x$cost.EMA4 <- x$n.EMA4 * 2 / x\$n.ABE names(x)[c(3, 5, 7, 9)] <- rep("cost", 4) print(signif(x, 4), row.names = FALSE)     CV n.FDA  cost n.EMA2  cost n.EMA4  cost n.ABE cost  0.052    30 5.000     12 1.000     12 2.000    12    1  0.096    18 3.000     20 1.667     12 2.000    12    1  0.186    16 2.286     70 5.000     34 4.857    14    1

In short: For low variability RSABE is more costly than the EMA’s fixed limits. If the CV is larger than ~12% it is the other way ’round.
Don’t forget the comparisons of variabilities. Whereas for the EMA’s approaches we assume homoscedasticity $$\small{(s_\textrm{wT}^2\equiv s_\textrm{wR}^2),}$$ in RSABE a test for unequal variances is part of the procedure (see Fig.3). Hence, I recommend a pilot study to avoid surprises.

• The EMA recommends 90.00–111.11% for AUC0–48 and 80.00–125.00% for Cmax. A replicate design is not required (though always acceptable).

Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

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