mmw
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India,
2015-02-09 11:12
(2327 d 10:51 ago)

Posting: # 14389
Views: 7,466
 

 Truncated 72 hours [NCA / SHAM]

Dear All,

We have conducted a truncated bio-equivalence study (truncation up to 72 hours as per European GL requirements). But it was found that around one-third of the study population did not turn up for 72.00 hours ambulatory sample.

Is their any impact on calculation of AUC due to number of missed sample? Could these subjects be excluded from statistical analysis? If yes, on what basis we exclude these subjects? However, this was not mentioned anywhere in guideline regarding exclusion of such subjects in truncated studies?

Thanks

MMW


Edit: Category changed. [Helmut]
Dr_Dan
★★  

2015-02-09 12:59
(2327 d 09:04 ago)

@ mmw
Posting: # 14390
Views: 6,486
 

 Truncated 72 hours

Dear MMW
Since the subjects did not show up for the 72 h sample you can not use these subjects for statistical evaluation. As a consequence you have less subjects and wider 90% CI. If the study passes everything is fine, if not you can argue by calculating AUCt for these subjects and all other subjects (AUCt=AUC72) and repeat ANOVA.
I hope this helps.
Kind regards
Dr_Dan

Kind regards and have a nice day
Dr_Dan
Helmut
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Vienna, Austria,
2015-02-09 16:10
(2327 d 05:53 ago)

@ mmw
Posting: # 14391
Views: 6,488
 

 Truncated 72 hours

Hi MMW,

» But it was found that around one-third of the study population did not turn up for 72.00 hours ambulatory sample.

Shit happens. Since AUC72 is a primary PK metric consider hospitalizing subjects in the future. By opting for ambulatory sampling you saved some rupees but risked to substantially loose power.

» Is their any impact on calculation of AUC due to number of missed sample? Could these subjects be excluded from statistical analysis? If yes, on what basis we exclude these subjects?

You should have asked yourself these questions before starting the study. It is good practice to expect the unexpected and state a bail-out procedure in the protocol. Whatever you do post hoc might smell fishy. :cherry picking:

» However, this was not mentioned anywhere in guideline regarding exclusion of such subjects in truncated studies?

Laking statements in guidelines are never an excuse for own judg­ments. Since Cmax likely is more variable than AUC, it might that you even pass BE after excluding subjects with missing AUC72 data. But one third is an awful lot…

[image]Not sure whether presenting BE of AUCt helps. If half lives were really long, maybe. Otherwise you end up with apples-and-oranges statistics.

In my protocols I state to use an estimated value anyway. This not only helps to correct for deviations from scheduled sampling times, but with ‘missings’ as well. In Phoe­nix/WinNonlin you could opt for a partial AUC0–72. In order to do so, you need a reliable esti­mate of λz (at least three data points after tmax).

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Helmut Schütz
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nobody
nothing

2015-02-09 22:48
(2326 d 23:16 ago)

@ Helmut
Posting: # 14393
Views: 6,411
 

 Truncated 72 hours

Hi everybody!

Just to add the obvious:

» ... In order to do so, you need a reliable esti­mate of λz (at least three data points after tmax).

...and really in the terminal phase, please... :-D

Kindest regards, nobody
mmw
☆    

India,
2015-02-11 12:57
(2325 d 09:06 ago)

@ nobody
Posting: # 14401
Views: 6,221
 

 Truncated 72 hours

Dear All,

Thanks for your reply.

MMW
stic-stats
☆    

India,
2020-07-13 14:22
(346 d 08:42 ago)

@ Helmut
Posting: # 21687
Views: 2,169
 

 Truncated 72 hours

Dear Helmut and All,

» » But it was found that around one-third of the study population did not turn up for 72.00 hours ambulatory sample.
»
» Shit happens. Since AUC72 is a primary PK metric consider hospitalizing subjects in the future. By opting for ambulatory sampling you saved some rupees but risked to substantially loose power.

May I know what type of power loose.
please clarify my dought.

Is their any impact on calculation of AUC due to number of missed sample? Could these subjects be excluded from statistical analysis? If yes, on what basis we exclude these subjects?

Regards,
Stic-Stats.


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. Standard quotes restored; see also this post #8[Helmut]
Helmut
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Vienna, Austria,
2020-07-13 17:07
(346 d 05:56 ago)

@ stic-stats
Posting: # 21689
Views: 2,157
 

 Truncated 72 hours

Hi stic-stats,

» » […] Since AUC72 is a primary PK metric consider hospitalizing subjects in the future. By opting for ambulatory sampling you saved some rupees but risked to substantially loose power.
»
» May I know what type of power loose.

Fewer eligible subjects will always decrease power. To which extent is unclear (mmw posted more than five years ago) and without the CV and sample size we would need a crystal ball.
Example (2×2 crossover, T/R 0.95, target power 0.80):

library(PowerTOST)
CV  <- seq(20, 40, 5) # intra-subject CV in percent
dor <- seq(0, 15, 5)  # dropout-rate in percent
res <- data.frame(CV = rep(CV, each = length(dr)), n = NA, dor = dor,
                  elig = NA, power = NA)
for (j in 1:nrow(res)) {
  tmp <- sampleN.TOST(CV = res$CV[j]/100, print = FALSE, details = FALSE)
  res$n[j] <- tmp[["Sample size"]]
  if (res$dor[j] == 0) {
    res$elig[j]  <- res$n[j]
    res$power[j] <- tmp[["Achieved power"]]
  } else {
    res$elig[j]  <- floor(tmp[["Sample size"]]*(100-res$dor[j])/100)
    res$power[j] <- suppressMessages(power.TOST(CV = res$CV[j]/100,
                                                 n = res$elig[j]))
  }
}
res$power <- signif(res$power, 5)
names(res)[1:4] <- c("CV (%)", "dosed", "dropouts (%)", "eligible")
print(res, row.names = FALSE)

Gives:

 CV (%) dosed dropouts (%) eligible   power
     20    20            0       20 0.83468
     20    20            5       19 0.81324
     20    20           10       18 0.79124
     20    20           15       17 0.76365
     25    28            0       28 0.80744
     25    28            5       26 0.77606
     25    28           10       25 0.75766
     25    28           15       23 0.71729
     30    40            0       40 0.81585
     30    40            5       38 0.79533
     30    40           10       36 0.77239
     30    40           15       34 0.74666
     35    52            0       52 0.80747
     35    52            5       49 0.78311
     35    52           10       46 0.75583
     35    52           15       44 0.73545
     40    66            0       66 0.80525
     40    66            5       62 0.77978
     40    66           10       59 0.75824
     40    66           15       56 0.73466


» Is their any impact on calculation of AUC due to number of missed sample?

Yes. Since generally studies are planned for the PK-metric with the highest variability (likely Cmax) the overall impact on power should be limited.

» Could these subjects be excluded from statistical analysis? If yes, on what basis we exclude these subjects?

Everything is possible – if stated in the statistical protocol. You could exclude them (only AUC whilst keeping Cmax), use an estimate (if λz is reliable), use the AUC to the last time point where you have concentrations for both T and R,…
Please search the forum; lots of posts.

Dif-tor heh smusma 🖖
Helmut Schütz
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stic-stats
☆    

India,
2020-07-15 06:17
(344 d 16:46 ago)

@ Helmut
Posting: # 21697
Views: 2,089
 

 Truncated 72 hours

Dear Helmut,

Thank you for your response. I am fresher in the field. few days back I joind BEBAC Forum.


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[Helmut]
M.tareq
☆    

2020-07-16 01:36
(343 d 21:27 ago)

@ Helmut
Posting: # 21706
Views: 2,039
 

 Truncated 72 hours

» In my protocols I state to use an estimated value anyway. This not only helps to correct for deviations from scheduled sampling times, but with ‘missings’ as well.

Is it possible to use a model-based approach to estimate the overall/typical value of Ke or CL and use that for prediction of such points ?

Or is it simpler to just perform a log-linear regression on averaged/all terminal points (3xTmax) and use that estimate to account for such deviations as defined in protocol.

Thanks

Mahmoud
Helmut
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Vienna, Austria,
2020-07-16 11:56
(343 d 11:07 ago)

@ M.tareq
Posting: # 21711
Views: 1,988
 

 Truncated 72 hours

Hi Mahmoud,

» Is it possible to use a model-based approach to estimate the overall/typical value of Ke or CL and use that for prediction of such points ?

Since PK modeling is not acceptable in BE, no. Furthermore, an average value might be misleading. Imagine the drug is subjected to polymorphic metabolism. The 90% extensive metabolizers have an average t½ of 4 hours and the 10% poor metabolizers 16 hours. You end up with an overall (geometric mean) t½ 4.59 hours. Now you could think about basing the decision to which group the subject belongs (i.e., instead of the average, use 4 or 16 hours) on the AUC. Spice the data with high between subject variability and you are at a loss.

» Or is it simpler to just perform a log-linear regression on averaged/all terminal points (3xTmax) and use that estimate to account for such deviations as defined in protocol.

That’s exactly what I do (and performed in Phoenix/WinNonlin when you specify a partial AUC0–72 with missings and/or deviations from the scheduled 72 hours sampling).

Dif-tor heh smusma 🖖
Helmut Schütz
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M.tareq
☆    

2020-07-16 17:05
(343 d 05:58 ago)

@ Helmut
Posting: # 21717
Views: 1,941
 

 Truncated 72 hours

» Since PK modeling is not acceptable in BE, no. Furthermore, an average value might be misleading. Imagine the drug is subjected to polymorphic metabolism. The 90% extensive metabolizers have an average t½ of 4 hours and the 10% poor metabolizers 16 hours. You end up with an overall (geometric mean) t½ 4.59 hours. Now you could think about basing the decision to which group the subject belongs (i.e., instead of the average, use 4 or 16 hours) on the AUC. Spice the data with high between subject variability and you are at a loss.

There have been some publications: Model‐based analyses of bioequivalence and Link : 2 about using model-based approach to drive BE, though as you said it's not acceptable in BE "yet", do you think in the near future we will see model based BE studies approved by regulators?
Helmut
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Vienna, Austria,
2020-07-16 18:30
(343 d 04:33 ago)

@ M.tareq
Posting: # 21719
Views: 1,942
 

 PK modeling in BE

Hi Mahmoud,

» There have been some publications: […] about using model-based approach to drive BE, though as you said it's not acceptable in BE "yet", do you think in the near future we will see model based BE studies approved by regulators?

I’m not very optimistic, esp. when dealing with missing data or – even worse – sparse sampling. Mats Karlson (quoted in your second reference) is extremely clever and France Mentré has my deepest respect when it comes to PopPK. However, last year at the 10th American Conference on Pharmacometrics (20–23 October, Orlando) she gave a presentation “Evaluation of Model Based BioEquivalence (MBBE) statistical approaches for sparse designs PK studies” with a whoopy mean (!) Type I Error of 7.6%. :-(

I hope that we can clarify things later this year.

Dif-tor heh smusma 🖖
Helmut Schütz
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mittyri
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Russia,
2020-07-19 00:24
(340 d 22:39 ago)

@ Helmut
Posting: # 21744
Views: 1,720
 

 PK modeling in BE

Hi Helmut,

» However, last year at the 10th American Conference on Pharmacometrics (20–23 October, Orlando) she gave a presentation “Evaluation of Model Based BioEquivalence (MBBE) statistical approaches for sparse designs PK studies” with a whoopy mean (!) Type I Error of 7.6%. :-(

Are you referring to this one?
Stan approach is a bit better, but still questionable

Kind regards,
Mittyri
Helmut
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Vienna, Austria,
2020-07-19 12:35
(340 d 10:29 ago)

@ mittyri
Posting: # 21749
Views: 1,683
 

 PK modeling in BE

Hi mittyri,

» Are you referring to this one?

Nope, an even worse one. I’ll send it to you by PM.
If you have nothing better to do, watch this recording (navigate to 01:27:02 for the presentation “Improved bioequivalence assessment through model-informed and model-based strategies”) of the 2020 Generic Drug Regulatory Science Initiatives Public Workshop.

Slide 14 (left) shows the Type I Errors for the standard NCA and the model based approach. Why should be even their average in NCA >5%? Sumfink stinks here.

Dif-tor heh smusma 🖖
Helmut Schütz
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