Amira Gouda
☆

Egypt,
2020-04-28 15:36
(413 d 19:07 ago)

(edited by mittyri on 2020-04-29 11:02)
Posting: # 21354
Views: 4,882

## Outlier in BE study [Study As­sess­ment]

Hi,
a BE study of standard design with truncated AUC 0-72, certain subject in one phase completed the samples up to 72 hours (test product) while in the other phase completed up to 12 hours (Reference product) the difference in AUC is very high (4 times) and the results including this subject dramatically changed to be failed
Now could we
1. Consider this subject as an outlier and represent the data without him?
2. Re-dose/Re-test this outlier subject again to test the validity for the outlying value?
3. perform another stage for the study (knowing that the study power and CV% were satisfactory)?

Are there any references support this issue?

Thanks
Amira

mittyri
★★

Russia,
2020-04-29 11:50
(412 d 22:53 ago)

@ Amira Gouda
Posting: # 21360
Views: 2,758

## Outlier in BE study

Hi Amira,

the study failed.
And common jurisdictions do not let you to cherrypick.
See here for some explanations.

Kind regards,
Mittyri
Ohlbe
★★★

France,
2020-04-29 15:19
(412 d 19:24 ago)

@ Amira Gouda
Posting: # 21362
Views: 2,725

## Outlier in BE study

Dear Amira,

» a BE study of standard design with truncated AUC 0-72, certain subject in one phase completed the samples up to 72 hours (test product) while in the other phase completed up to 12 hours (Reference product)

Do you mean to say that these subjects did not have any sample collected later than 12 hours, or that their concentration was BLQ after 12 hours ?

In the first case: this would provide grounds for excluding them from the stats (should be pre-specified in the protocol). In the second case: you're in trouble.

Regards
Ohlbe
Amira Gouda
☆

Egypt,
2020-04-29 16:35
(412 d 18:08 ago)

@ Ohlbe
Posting: # 21364
Views: 2,732

## Outlier in BE study

Dear Ohlbe

» Do you mean to say that these subjects did not have any sample collected later than 12 hours, or that their concentration was BLQ after 12 hours ?

ِActually, the first case is our case the subject did not have any sample collected later than 12 hours which occurred in phase 1 (and he consumed reference product),
our protocol stated that dropouts and withdrawals will be included in pk, not statistics.
the subject had completed the 2 phases due to sample size issues also the product half-life is a wide range, not a definite number.
we analyzed all samples, but now:
1. if we exclude the subject and when submitting the study to RA, is this exclusion will be not accepted as the results of statistics including him is highly different?
2. I will need supportive data from guidelines for this issue specifically, could you please help me if there is?
3. Is re-dosing/re-testing of the subject with controls, could be supportive in this situation?
Thanks

Edit: Full quote removed. Please delete everything from the text of the original poster which is not necessary in understanding your answer; see also this post #5! [Ohlbe]
Ohlbe
★★★

France,
2020-04-29 17:02
(412 d 17:42 ago)

@ Amira Gouda
Posting: # 21365
Views: 2,713

## Missing samples

Dear Amira,

» ِActually, the first case is our case the subject did not have any sample collected later than 12 hours [...]

What was the reason why he didn't have any sample collected after 12 hours ?

Regards
Ohlbe
Amira Gouda
☆

Egypt,
2020-04-29 17:08
(412 d 17:35 ago)

@ Ohlbe
Posting: # 21366
Views: 2,717

## Missing samples

Dear Ohlbe,

» What was the reason why he didn't have any sample collected after 12 hours ?

he did not attend the samples collection for 24, 48 and 72 hours intervals (as the study protocol stated that the hospitalization lasts only for 12 hours)

thanks
ElMaestro
★★★

Denmark,
2020-04-29 17:21
(412 d 17:23 ago)

@ Amira Gouda
Posting: # 21367
Views: 2,689

## Missing samples

Hi Amira Gouda,

» he did not attend the samples collection for 24,48 and 72 hours intervals (as the study protocol stated that the hospitalization lasts only for 12 hours)

BE is generally done on the PP population, not on the ITT.
This guy is clearly not participating PP, his AUC is not outright a meaningful indicator of product performance, so out he goes, I think.

Pass or fail!
ElMaestro
Amira Gouda
☆

Egypt,
2020-05-01 23:50
(410 d 10:54 ago)

@ ElMaestro
Posting: # 21383
Views: 2,553

## Missing samples

Hi ElMaestro,

» This guy is clearly not participating PP, his AUC is not outright a meaningful indicator of product performance, so out he goes, I think.

Yes, he actually missed the terminal phase, his AUC0-t is about 17% of his AUC0-inf, also check his figure

What we could do?

1- Extrapolate to 72 h
2- Use AUC common (0-12)
3- or based on the results of the current study could we make another stage on 12 subjects (knowing that the first one was performed on 24)?
4- Exclude the subject from stat evaluation of AUC(0-t) and include in all other parameters (as long as it does not jeopardize the acceptance of the study)

thanks
Helmut
★★★

Vienna, Austria,
2020-05-02 11:35
(409 d 23:08 ago)

@ Amira Gouda
Posting: # 21387
Views: 2,527

## Options

Hi Amira,

» Yes, he actually missed the terminal phase, his AUC0-t is about 17% of his AUC0-inf, also check his figure
»
»

THX, very helpful. Comparing the profiles of both treatments around ten hours (does the drug undergo enterohepatic recycling?) clearly shows that the terminal phase is not reached. Given what we see for T – whatever the software suggests – I would say it starts at 24 hours. Since you cannot estimate λz for R, your 17% are too low. True number? Impossible to tell.

» What we could do?
»
» 1- Extrapolate to 72 h

No (unknown λz).

» 2- Use AUC common (0-12)

Makes sense to me. Regulatory acceptance unclear (not stated in any guideline so far).

» 3- or based on the results of the current study could we make another stage on 12 subjects (knowing that the first one was performed on 24)?

No. Three reasons (regulatory, ethical, statistical):
1. The intention to perform a two-stage design would have to be stated in the protocol together with the planned method and adjusted α <0.05. You can’t change the design because you don’t like the original outcome.
2. Your sample size estimation was based on a fixed sample design with α 0.05. A TSD likely require more subjects, which was not approved by the ethics committee.
3. If you already assessed the study (say, after excluding the subject) the entire α was already ‘consumed’. Nothing is ‘left’ for a pooled analysis.
Even if you didn’t assess the study for BE yet, #1 and #2 are still problematic. Requires an amendment to the SAP and approval by both parties. Especially the agency will think that’s fishy.

» 4- Exclude the subject from stat evaluation of AUC(0-t) and include in all other parameters (as long as it does not jeopardize the acceptance of the study)

IMHO, the most realistic approach. This is what I do in my studies for decades. But again, all conditions for exclusion – detailed! – stated in the protocol. Mine have one entire page of definitions (vomiting, diarrhea, missing samples, dealing with time deviations, unreliable λz, etc.), which will lead to the ITT- (PK insofar possible, even for dropouts) and PP-datasets.
Whether a post hoc exclusion will be accepted, no idea. But yes, I agree with ElMaestro that the subject did not follow the protocol and therefore, might be excluded from the comparison of AUC (only).
Cmax is still reliable because you had all concentrations before tmax and five decreasing ones after it. Since Cmax generally is more variable than AUC, likely the study was powered for it and you might easily pass AUC:

library(PowerTOST) CV.AUC  <- seq(0.10, 0.25, 0.025) CV.Cmax <- CV.AUC * 1.5 # arbitrary but higher than ones of AUC res     <- data.frame(CV.AUC = CV.AUC, CV.Cmax = CV.Cmax, n = NA,                       pwr.Cmax = NA, pwr.AUC = NA,                       elig.AUC = NA, pwr.AUC.elig = NA, pwr.loss.pct = NA) theta0  <- 0.95 # assumed T/R-ratio of both PK metrics target  <- 0.80 # ≥80% power for (j in 1:nrow(res)) {   tmp         <- sampleN.TOST(CV = CV.Cmax[j], theta0 = theta0,                               targetpower = target, print = FALSE)   res[j, 3:4] <- tmp[7:8]          # Sample size, achieved power   res[j, 5]   <- power.TOST(CV = CV.AUC[j], theta0 = theta0, n = res$n[j]) res[j, 6] <- res$n[j] - 1      # AUC: one less eligible subject   res[j, 7]   <- suppressMessages( # we know that it’s unbalanced                    power.TOST(CV = CV.AUC[j], theta0 = theta0, n = res[j, 6]))   res[j, 8]   <- 100 * (res[j, 7] - res[j, 5]) / res[j, 5] } print(signif(res, 4), row.names = FALSE)  CV.AUC CV.Cmax  n pwr.Cmax pwr.AUC elig.AUC pwr.AUC.elig pwr.loss.pct   0.100  0.1500 12   0.8305  0.9883       11       0.9800      -0.8473   0.125  0.1875 18   0.8386  0.9896       17       0.9854      -0.4216   0.150  0.2250 24   0.8227  0.9868       23       0.9833      -0.3468   0.175  0.2625 32   0.8247  0.9868       31       0.9844      -0.2436   0.200  0.3000 40   0.8158  0.9848       39       0.9828      -0.2098   0.225  0.3375 48   0.8022  0.9815       47       0.9796      -0.1981   0.250  0.3750 58   0.8007  0.9807       57       0.9790      -0.1668

As usual the impact of dropouts – or in your case of an excluded subject – on power is pretty low. Have a look at the function pa.ABE() of PowerTOST and the example in its vignette.

Dif-tor heh smusma 🖖
Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes
Amira Gouda
☆

Egypt,
2020-05-03 21:15
(408 d 13:28 ago)

@ Helmut
Posting: # 21388
Views: 2,293

## Options

Thank you very much, it is really helpful