Clarifications to the Dangerous nonsense [Power / Sample Size]
❝ If you are in fact Someswara Rao please register to the forum and post with your own account.
My account is created, response is provided.
❝ ❝ Please do not underestimate the article by looking at journal name.
❝ […] I judged it based on its – rather doubtful – content.
Detlew commented as mentioned below and hence I responded.
❝ ❝ ❝ ❝ ❝ “Forget this "gem" of science literature. It's simply crap. Don't believe even in comma of this paper. Throw it in a high arc into your waste bin”.
Based on the formula given in “design and analysis of BA and BE Studies, SHEIN-Chung CHOW, JEN-PEI LIU”, Refer Page no: 153)”, the only “log of bioequivalence limit” need to be substituted to obtain the required sample size. Rest all other parameters in the formula can be filled based on the pilot/historical data.
❝ ❝ You all are requested to see the formulas mentioned to arrive at sample size estimations, since it is pure Mathematics.
❝ Please note that a correct (though approximate) formula applied wrongly (e.g., to a false model) gives still a wrong result.
The correct formula never applied wrongly here; instead it was used as base for the sample size estimation. The reference for this is “design and analysis of BA and BE Studies, SHEIN-Chung CHOW, JEN-PEI LIU”), Refer Page no: 153.
❝ ❝ You all believe following is the formula for sample size estimation?
❝ I cannot speak for the others, but myself for 2×2×2 crossovers and ABE (!) as a last resort, yes. Note that the formula is based on the shifted central t-distribution which is an approximation of the noncentral] t-distribution which itself approximates the exact method (Owen’s Q-function). Why not use a better method? But we are not discussing 2×2×2 crossovers here, right?
I don’t have to respond for this.
❝ ❝ Please be noted that the same formula used worldwide for sample size estimation.
❝ Hopefully not for reference-scaling! Due to the conditions of the frameworks (scaling applicable only if CVwR >30%, GMR restriction of 0.8–1.25, different σ0 for the FDA and the EMA, upper cap at CVwR of 50% for the EMA) you must not simply plug the parameters you mentioned into any (‼) formula.
We do understand that the requirements of HVDs for both FDA and EMA are different. Hence to obtain the log of bioequivalence with respective to FDA and EMA separately, which need to be substituted in the formula, we have explained all the calculations in the paper.
❝ Power and, therefore, sample sizes are not directly accessible – we need to perform Monte Carlo simulations! I suspect you did not understand the paper by Tóthfálusi and Endrényi. […] Due to the slow convergence (example) one needs to simulate ≥105 BE studies for any given GMR, CV, and target power. This should be taken into account if comparing results with the article of the two Lászlós (only 10,000 simulations; hence, power might have been not sufficiently stable).
Simulation with ≥105 BE studies is not possible with us, instead it is pure formula based one. In general sample size calculations are done using 80% power requirements; however, the post hoc power is not at all important, as long as you bioequivalence limits are within the acceptance range.
❝ ❝ (In general 10% difference is considered. In case of highly variable drugs go for 5% difference of Generic Vs Innovator)
❝ Exactly the opposite. In ABE and low to moderate CVs ≥5% can reasonably be considered. In the case of HVDs/HVDPs Tóthfálusi and Endrényi recommend 10%. A larger deviation than the one commonly used with drugs with low to moderate variability should be assumed regardless whether scaling is intended or not. It is an intrinsic property of HVD(P)s that the GMR varies between studies.
In general, a generic player will always try to match innovator. To reserve the resources and to ensure the probability of success (to pass the study), a 10% difference can be considered low to moderate variable drugs as it demand less sample size and a 5% difference is always appropriate for highly variable drugs as it demands more sample size due high variability.
Kindly note that, as long as the test versus reference difference increases, more sample size is required to prove the bioequivalence. I don’t advice to go with 10% difference as unpredictable behavior will be observed HVDs as well as it requires large sample size. The probability of success will be very high with 5% difference while ensuring the resources conservative instead of 10% test versus reference difference and I don’t want to compare with Tóthfálusi and Endrényi in this case.
❝ ❝ We noted that the incomplete sentence in the article and suggested to delete the same before publishing, but unfortunately it was not deleted by the Editor.
❝ Why the heck did you want to delete the truncated sentence? It should have been the other way ’round: Complete it. References 6–8 are not linked to anything in the article. So likely more text is missing.
We would like to explain that, “to obtain within subject standard deviation of reference product (SWR) or within subject variability of reference product by using following formula” and it is obtained after rearranging the equation mentioned in page no: 17 of 27 in the EU directives of Doc. Ref.: CPMP/EWP/QWP/1401/98 Rev. 1/ Corr **
Swr = Sqrt [ln (CV)2+1]
Reference 6 is used to obtain the above mentioned formula correlating within subject standard deviation of reference product (SWR) or within subject variability of reference product.
Reference 7 is used is just to describe formula given in “design and analysis of BA and BE Studies, SHEIN-Chung CHOW, JEN-PEI LIU”, Refer Page no: 153)”,
Reference 8 is used is just to describe the initial introduction part.
❝ ❝ Moreover, we have validated obtained sample size estimations against the published article “Sample Sizes for Designing Bioequivalence Studies for Highly Variable Drugs, J Pharm Pharmaceut Sci (www.cspsCanada.org) 15(1) 73 - 84, 2011”.
❝ You can’t be serious! […] Can you please specify what you mean by “validated”?
We have compared the obtained sample size by using our method with Tóthfálusi & Endrényi (2012) method and it was identified that the obtained sample size did not differed significantly using EMA approach. However it was differed using FDA method. This difference is expected as our approach is the formula based one as the calculations are different here due to change in the regulatory constants.
❝ ❝ The same article did not specify the sample size estimations beyond 60% ISCV and above.
❝ Wrong again. All tables give sample sizes for up to 80% CV.
Agreed.
❝ […] I will make you understand.
❝ How; what do you suggest? Brainwashing, waterboarding, hire a contract killer?
What I mean is how we have arrived at these calculations.
❝ ❝ […] please provide sample size estimation formula for scaled average bioequivalence studies from your end …
❝ There is none.
❝ If you hope that miraculously you will be able to derive an analytical solution, fantastic. Go ahead and submit it to a real journal. Become famous.
I am confident that the formula used will definitely works, since I did not manipulated anything and the same formula know to everyone in the pharmaceutical industry.
❝ ❝ … and educate the pharmaceutical world.
❝ Mission accomplished.
❝ @Someswara: I hope you have the guts to retract this article – if that’s possible at a predatory journal at all.
I don’t have to retract this article since nothing is manipulated. Of course there are some typo errors I accepted the same. Again what are all questions raised by you all are answered.
❝ Since recentscientific.com and thus site have the same PageRank (4) I hope that colleagues interested in sample size estimation for reference-scaling will find not only your article but this thread as well.
I am not bothered about the ranking of journals; the intention was to reveal the sample size justification formula for SABE studies.
Best Regards
Someswara Rao
Edit: Full quotes removed. Please delete everything from the text of the original poster which is not necessary in understanding your answer; see also this post! The forum’s standard quote system restored. [Helmut]
Complete thread:
- Sample Size estimation for Replicate Cross over studies FDA or EMA criteria Lara 2016-02-10 00:46 [Power / Sample Size]
- Garbage d_labes 2016-02-10 08:34
- Ponzi scheme Helmut 2016-02-10 20:42
- Ponzi scheme Lara 2016-02-11 20:35
- Ponzi scheme jag009 2016-02-16 19:55
- Ponzi scheme Helmut 2016-02-17 12:29
- Ponzi scheme sanketh.gupta 2016-03-07 06:21
- Education necessary d_labes 2016-03-07 11:20
- Dangerous nonsense Helmut 2016-03-07 14:21
- Clarifications to the Dangerous nonsensesomu_korla 2016-03-09 08:17
- Absurd absurdity d_labes 2016-03-09 09:46
- Outright bizarre Helmut 2016-03-09 16:02
- Clarifications to Outright bizarre somu_korla 2016-03-10 08:44
- Science vs. fairy tales Helmut 2016-03-10 18:58
- Clarifications for Science vs. fairy tales somu_korla 2016-03-11 10:00
- Forlorne hope d_labes 2016-03-11 16:30
- Forlorne hope lechia 2016-08-05 22:52
- Forlorne hope nobody 2016-08-08 15:49
- Forlorne hope d_labes 2016-08-09 08:50
- Forlorne hope nobody 2016-08-08 15:49
- Forlorne hope lechia 2016-08-05 22:52
- Science vs. fairy tales Helmut 2016-03-10 18:58
- Clarifications to Outright bizarre somu_korla 2016-03-10 08:44
- Clarifications to the Dangerous nonsensesomu_korla 2016-03-09 08:17
- Ponzi scheme sanketh.gupta 2016-03-07 06:21
- Ponzi scheme Helmut 2016-02-17 12:29