balakotu ★ India, 2015-07-02 11:05 (3192 d 15:23 ago) Posting: # 15013 Views: 9,163 |
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Dear all, http://www.hc-sc.gc.ca/dhp-mps/consultation/drug-medic/be-hvdp-notice-avis-nb-pphv-eng.php Regards Kotu |
d_labes ★★★ Berlin, Germany, 2015-07-02 11:19 (3192 d 15:09 ago) @ balakotu Posting: # 15015 Views: 8,584 |
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Dear Kotu, THX for that link. Interesting! EMA approach but allowed for AUC. Since Cmax doesn't need a CI criterion at all, only a point estimate restriction is imposed. — Regards, Detlew |
Helmut ★★★ Vienna, Austria, 2015-07-02 15:20 (3192 d 11:08 ago) @ balakotu Posting: # 15017 Views: 8,383 |
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Hi Kotu, THX for the information. Why the heck: The test product (T) should be administered either once in a 3-period design (RTR, TRR, RRT) or twice in a 4-period design (TRTR, RTRT). No full replicate 3-period design (TRT|RTR)? See this lengthy thread. Disappointing.— Dif-tor heh smusma 🖖🏼 Довге життя Україна! Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes |
Lucas ★ Brazil, 2015-07-02 15:35 (3192 d 10:53 ago) @ Helmut Posting: # 15019 Views: 8,394 |
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Hello my friends ❝ Why the heck:[...] Yes! I do not like partial replicate designs also. I have a feeling that, taking those converging problems aside, having 2x more data of one treatment than the other is not good for the estimations. Seems like you give more chances for one treatment to show its "real PK profile". Could I say that? Thx for the link. Lucas |
Helmut ★★★ Vienna, Austria, 2015-07-02 18:41 (3192 d 07:47 ago) @ Lucas Posting: # 15022 Views: 8,420 |
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Hi Lucas, ❝ I do not like partial replicate designs also. I have a feeling that, taking those converging problems aside, … To me it is not clear which statistical model HC prefers (see this post). Sounds like a mixed-effects model (FDA’s 2001 guidance, FDA’s progesterone guidance / unscaled ABE, EMA’s Method C) to me. Only then one may run into convergence issues. No problem with EMA’s crippled models (Methods A [subjects fixed] and B [subjects random]). ❝ … having 2x more data of one treatment than the other is not good for the estimations. Seems like you give more chances for one treatment to show its "real PK profile". Could I say that? You can, of course. This forum is not censored. — Dif-tor heh smusma 🖖🏼 Довге життя Україна! Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes |
Lucas ★ Brazil, 2015-07-02 20:06 (3192 d 06:22 ago) @ Helmut Posting: # 15025 Views: 8,338 |
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Hi Helmut! ❝ You can, of course. This forum is not censored. Maybe ESL is to blame here. What I meant is "does that statement makes sense?". Thx. Lucas Teixeira |
drgunasakaran1 ★★ 2015-07-02 19:08 (3192 d 07:20 ago) @ balakotu Posting: # 15023 Views: 8,394 |
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Dear Mr Kotu, Thanks for sharing the link. It's surprising to see that the HVDP classification is based on the within subject coefficient of variation of AUC alone and not based on AUC and/or Cmax. — Dr S Gunasakaran MBBS MD Disclaimer: The replies/posts are my personal opinions and it does not represent my company views on the same. |
Helmut ★★★ Vienna, Austria, 2015-07-02 19:37 (3192 d 06:51 ago) @ drgunasakaran1 Posting: # 15024 Views: 8,314 |
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Dear Dr. S. Gunasakaran, ❝ It's surprising to see that the HVDP classification is based on the within subject coefficient of variation of AUC alone and not based on AUC and/or Cmax. Last year HC considered FDA’s approach (see here). On many previous occasions HC stated that they don’t see any need for changing the requirement for Cmax since for more than two decades only the T/R-ratio had to be contained within 80–125%, which – in HC’s opinion – was never an obstacle considering the necessary sample sizes. László Endrényi and László Tóthfalusi showed many times that for high CVs (say ≥50%) only the restriction on the point estimate leads the BE-decision (irrespective of the scaling method). Therefore, I understand the logic behind:
— Dif-tor heh smusma 🖖🏼 Довге життя Україна! Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes |
d_labes ★★★ Berlin, Germany, 2015-07-03 10:21 (3191 d 16:07 ago) @ balakotu Posting: # 15028 Views: 8,177 |
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Dear all, another interesting detail: All (widened) acceptance ranges given with one decimal, even the conventional 80 - 125%. — Regards, Detlew |
Helmut ★★★ Vienna, Austria, 2015-07-03 13:55 (3191 d 12:33 ago) @ d_labes Posting: # 15032 Views: 8,214 |
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Dear Detlew, that’s consistent with HC’s other current requirements (generally 80.0–125.0%, critical dose drugs 90.0–112.0%). — Dif-tor heh smusma 🖖🏼 Довге життя Україна! Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes |
d_labes ★★★ Berlin, Germany, 2015-07-03 14:53 (3191 d 11:35 ago) @ Helmut Posting: # 15033 Views: 8,126 |
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Dear Helmut, thanks for reminding me. ❝ critical dose drugs 90.0–112.0%). Although this is a bit of generous rounding to one decimal — Regards, Detlew |
Helmut ★★★ Vienna, Austria, 2015-07-03 16:49 (3191 d 09:39 ago) @ d_labes Posting: # 15035 Views: 8,191 |
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Dear Detlew, ❝ ❝ critical dose drugs 90.0–112.0%). ❝ ❝ Although this is a bit of generous rounding to one decimal I told Eric Ormsby at the last BioInternational conference (London, October 2008) that on the long run the mean of generic NTIDs will be \(\sqrt{0.900 \times 1.120} = 1.003992 \ldots\). IIRC he replied “We can stand that. 112% is just easier to remember.”… When discussions were hot in the mid-1980s (switching from untransformed to log-transformed data) we had a width of the acceptance range of 0.40 (80–120%) based on an acceptable Δ of 0.20:
— Dif-tor heh smusma 🖖🏼 Довге життя Україна! Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes |
ElMaestro ★★★ Denmark, 2015-07-03 15:02 (3191 d 11:26 ago) @ Helmut Posting: # 15034 Views: 8,109 |
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Hi all, I am still baffled by scaling, whichever way it is done. Granted, I do not understand much of BE and in particular I am struggling with scaling. I am baffled for this reason: As an alternative to scaling we might in stead relax the alpha and keep accepting and rejecting the same products. We would achieve not just roughly the same but exactly the same because scaling keeps the GMR unaffected. A practical matter is of course to identify the mathematical relationship between the scaling constant of acceptance range and scaling of alpha but this is nothing more than a practicality (find a closed form or chose empery, whatever works for you). I think the only reason not to formulate the problem (=acceptance vs rejection) in terms of alpha relaxation but rather in terms of widening of CI limits is that if we formulate it as a widening of alpha level then we need to admit explicitly what scaling involves: An occasional increase in type I error. Prove me wrong, please. — Pass or fail! ElMaestro |
Helmut ★★★ Vienna, Austria, 2015-07-03 17:38 (3191 d 08:50 ago) @ ElMaestro Posting: # 15036 Views: 8,247 |
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Ahoy! ❝ As an alternative to scaling we might in stead relax the alpha and keep accepting and rejecting the same products… I disagree. The BE-problem is originally stated in terms of the maximum acceptable difference (Δ), where Δ is considered to be clinically not relevant. This Δ leads to the acceptance range (see the history lesson above). If a a drug/drug product is highly variable it will be very difficult to demonstrate BE of the reference to itself given the conventional Δ of 20%. On the other hand, practically all HVDs/HVDPs have a flat dose response curve (i.e., even a large Δ is not expected to be clinically relevant). Hence, it makes sense to widen the acceptance range (either to a fixed value in some jurisdictions or scaled based on the swR in others). If we modify the AR, we can still keep the patient’s risk. I know of only one regulation where the α is modified. ANVISA requires for NTIDs the conventional AR but assessed by a 95% CI. IMHO, that’s a flawed approach. Yes, the risk will be lower. But which risk? To have 2.5% of the patient-population with a BA of <80% and 2.5% with >125%. Can be nasty for NTIDs. I don’t get the “idea”. ❝ I think the only reason not to formulate the problem (=acceptance vs rejection) in terms of alpha relaxation but rather in terms of widening of CI limits is that if we formulate it as a widening of alpha level then we need to admit explicitly what scaling involves: An occasional increase in type I error. An inflated TIE could easily (!) be avoided by either pre-specifying a lower α (0.025 or 0.0304 dependent on the design) or adjusting α based on swR. Time to publish sumfink. Even then, one big problem – common to all RSABE-methods – remains open. Products are approved according to different standards. Furthermore, the procedure is not reversible. In ABE we can recode T with R and the decision (pass/fail) will always be the same. This is not the case in RSABE if swR ≠ swT (of course we need a fully replicated design to show that). I think that the two Lászlós in one of their papers mentioned that it would be desirable that agencies pool swR-data and publish a fixed widened acceptance range in product-specific guidelines. In such a case the Null-Hypothesis is no more modified “in face of the data” and no inflation of the TIE is possible. No replicate designs, no fiddling around with crappy models. A conventional 2×2 would do the job (back to the roots). This is what I hope for (ha-ha): RSABE as a kind of “transition state” until enough data are collected allowing regulators to come up with justified recommendations. Will never happen. I know, I know. — Dif-tor heh smusma 🖖🏼 Довге життя Україна! Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes |
ElMaestro ★★★ Denmark, 2015-07-03 18:35 (3191 d 07:53 ago) @ Helmut Posting: # 15037 Views: 8,007 |
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Hi Helmut, I see your points, however, but how did they relate to my point? I reckon we address two different aspects. It is likely a question of bad explanations on my part. Consider this:
My point was/is that changing the acceptance range for the CI in mathematical terms does exactly the same as keeping the acceptance range while changing the applied alpha. — Pass or fail! ElMaestro |
Helmut ★★★ Vienna, Austria, 2015-07-03 18:44 (3191 d 07:44 ago) @ ElMaestro Posting: # 15038 Views: 8,028 |
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Hi ElMaestro, ❝ – For any CV measured I can define a revised alpha that keeps the original acceptance range in place (80.00%-125.00%), and which achieves exactly the same (it rejects and accepts exactly the same products as your approach). Yep, but (big but): Your α will necessarily be (much) larger than 0.05. Let’s not warm-up the dispute RA Fisher had with Jerzy Neyman. At least I don’t want to go there. ❝ I can probably work out an example if you need one? THX, I do understand what you mean. ❝ My point was/is that changing the acceptance range for the CI in mathematical terms does exactly the same as keeping the acceptance range while changing the applied alpha. Sure. Would you prefer to say “The risk for the patient to have a BA outside 80–125% of the reference is 31.42%”? — Dif-tor heh smusma 🖖🏼 Довге життя Україна! Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes |
d_labes ★★★ Berlin, Germany, 2015-07-03 23:11 (3191 d 03:17 ago) @ Helmut Posting: # 15039 Views: 7,978 |
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Gents (Helmut & Ol'Pirate)! Relax and enjoy the mediteran weather in Europe. Seems we get Greek conditions here, hopefully not the monetary one . Whatch the scientific quote for this day (I will copy it for you since next day it is gone): Statistics: The only science that enables different experts using the same figures to draw different conclusions. Full stop. SCRN . — Regards, Detlew |