mmw ☆ India, 20150209 12:12 (3355 d 05:25 ago) Posting: # 14389 Views: 9,916 

Dear All, We have conducted a truncated bioequivalence study (truncation up to 72 hours as per European GL requirements). But it was found that around onethird 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 ★★ Germany, 20150209 13:59 (3355 d 03:38 ago) @ mmw Posting: # 14390 Views: 8,536 

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 ★★★ Vienna, Austria, 20150209 17:10 (3355 d 00:27 ago) @ mmw Posting: # 14391 Views: 8,555 

Hi MMW, ❝ But it was found that around onethird of the study population did not turn up for 72.00 hours ambulatory sample. Shit happens. Since AUC_{72} 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 bailout procedure in the protocol. Whatever you do post hoc might smell fishy. ❝ 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 C_{max} likely is more variable than AUC, it might that you even pass BE after excluding subjects with missing AUC_{72} data. But one third is an awful lot… Not sure whether presenting BE of AUC_{t} helps. If half lives were really long, maybe. Otherwise you end up with applesandoranges 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 AUC_{0–72}. In order to do so, you need a reliable estimate of λ_{z} (at least three data points after t_{max}). — Diftor heh smusma 🖖🏼 Довге життя Україна! _{} Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 
nobody nothing 20150209 23:48 (3354 d 17:49 ago) @ Helmut Posting: # 14393 Views: 8,464 

Hi everybody! Just to add the obvious: ❝ ... In order to do so, you need a reliable estimate of λ_{z} (at least three data points after t_{max}). ...and really in the terminal phase, please... — Kindest regards, nobody 
mmw ☆ India, 20150211 13:57 (3353 d 03:40 ago) @ nobody Posting: # 14401 Views: 8,262 

Dear All, Thanks for your reply. MMW 
sticstats ☆ India, 20200713 16:22 (1374 d 02:15 ago) @ Helmut Posting: # 21687 Views: 4,225 

Dear Helmut and All, ❝ ❝ But it was found that around onethird of the study population did not turn up for 72.00 hours ambulatory sample. ❝ ❝ Shit happens. Since AUC_{72} 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, SticStats. 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 ★★★ Vienna, Austria, 20200713 19:07 (1373 d 23:30 ago) @ sticstats Posting: # 21689 Views: 4,198 

Hi sticstats, ❝ ❝ […] Since AUC_{72} 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):
❝ Is their any impact on calculation of AUC due to number of missed sample? Yes. Since generally studies are planned for the PKmetric with the highest variability (likely C_{max}) 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 C_{max}), use an estimate (if λ_{z} 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. — Diftor heh smusma 🖖🏼 Довге життя Україна! _{} Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 
sticstats ☆ India, 20200715 08:17 (1372 d 10:20 ago) @ Helmut Posting: # 21697 Views: 4,115 

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 ☆ 20200716 03:36 (1371 d 15:01 ago) @ Helmut Posting: # 21706 Views: 4,101 

❝ 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 modelbased 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 loglinear regression on averaged/all terminal points (3xTmax) and use that estimate to account for such deviations as defined in protocol. Thanks Mahmoud 
Helmut ★★★ Vienna, Austria, 20200716 13:56 (1371 d 04:41 ago) @ M.tareq Posting: # 21711 Views: 4,096 

Hi Mahmoud, ❝ Is it possible to use a modelbased 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 t_{½} of 4 hours and the 10% poor metabolizers 16 hours. You end up with an overall (geometric mean) t_{½} 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 loglinear 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 AUC_{0–72} with missings and/or deviations from the scheduled 72 hours sampling). — Diftor heh smusma 🖖🏼 Довге життя Україна! _{} Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 
M.tareq ☆ 20200716 19:05 (1370 d 23:32 ago) @ Helmut Posting: # 21717 Views: 4,006 

❝ 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 t_{½} of 4 hours and the 10% poor metabolizers 16 hours. You end up with an overall (geometric mean) t_{½} 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 modelbased 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 ★★★ Vienna, Austria, 20200716 20:30 (1370 d 22:07 ago) @ M.tareq Posting: # 21719 Views: 3,969 

Hi Mahmoud, ❝ There have been some publications: […] about using modelbased 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 10^{th} 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. — Diftor heh smusma 🖖🏼 Довге життя Україна! _{} Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 
mittyri ★★ Russia, 20200719 02:24 (1368 d 16:13 ago) @ Helmut Posting: # 21744 Views: 3,781 

Hi Helmut, ❝ However, last year at the 10^{th} 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 ★★★ Vienna, Austria, 20200719 14:35 (1368 d 04:02 ago) @ mittyri Posting: # 21749 Views: 3,766 

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 modelinformed and modelbased 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. — Diftor heh smusma 🖖🏼 Довге життя Україна! _{} Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 