BEproff ● 20161228 09:14 (2461 d 14:36 ago) Posting: # 16892 Views: 8,851 

Hi All, Let's say I want to calculate sample size based on results of a previous study. I have the following data: n=20 CV=0.18 (for Cmax and AUC) GMR1 = 0.97 GMR2=1.19 If to calculate size based on GMR1 I think it's better to use a script: sampleN.TOST(CV=0.18, theta0=0.95) if to use GMR = 1.19 we see something terrible: sampleN.TOST(CV=0.18, theta0=1.19)  too many people...What to do in such situations? 
mittyri ★★ Russia, 20161228 10:17 (2461 d 13:34 ago) @ BEproff Posting: # 16893 Views: 7,997 

Hi BEproff, Out of curiosity: what is the reason not to use the detailed Helmut's answer to your previous similar question here? — Kind regards, Mittyri 
BEproff ● 20161228 11:35 (2461 d 12:15 ago) @ mittyri Posting: # 16894 Views: 7,956 

Hi mittyri, That was a post about pooled CV. How would you answer the question? 
mittyri ★★ Russia, 20161228 12:38 (2461 d 11:12 ago) @ BEproff Posting: # 16896 Views: 7,963 

Hi BEproff, My answer is: 'Educated Guess' No one will decide which GMR you should use behind your back. Fully ACK the ElMaestro's answer below. I'd cite another Master's post: ❝ If you intend the next study to be pivotal then the choice of GMR guess should reflect a ton of factors including biology, dissolution, gut feeling, Saturn's moons, your biorhythms, personal horoscope (you've gotta ask yourself one question: "Do I feel lucky?" Well, do ya, punk?) etc but not your management's desire to save money. Please also think about GMR = 1.19 Would you recommend to use the formulation with REAL GMR = 1.19 in practice? — Kind regards, Mittyri 
ElMaestro ★★★ Denmark, 20161228 11:52 (2461 d 11:59 ago) @ BEproff Posting: # 16895 Views: 8,156 

Dear BEproff, ❝ I have the following data: ❝ n=20 ❝ CV=0.18 (for Cmax and AUC) ❝ GMR1 = 0.97 ❝ GMR2=1.19 ❝ ❝ If to calculate size based on GMR1 I think it's better to use a script: ❝ (...) if to use GMR = 1.19 we see something terrible: ❝ (...) What to do in such situations? If your product has a true GMR of 1.19 for a primary ratio, then 166 may be a sample that meets your power requirement size. However, bear in mind that such calculations involve models with assumptions, notably a Normal distribution on the log scale. Your GMR from the trial is an estimate and not a true value; a good guess is that there is 50% chance the true GMR is worse. In this paper, the author tried to look into the various options for situations like yours: How to use the result from one trial in the planning of the next? The answer was: You have several options, but you do not get any easy solution that allows high power and low sample size if you try to make use of the observed GMR (and that, by the way, is also why pilot trials are not good approaches for telling if candidate formulations are so promising that a pivotal trial is merited). And if you gamble and use a GMR for planning that is closer to 1.0 than what you observed previously then you are also easily screwing yourself. — Pass or fail! ElMaestro 
d_labes ★★★ Berlin, Germany, 20161228 14:42 (2461 d 09:09 ago) @ BEproff Posting: # 16897 Views: 8,103 

Dear BEproff, dear All, ❝ Let's say I want to calculate sample size based on results of a previous study. ❝ ❝ I have the following data: ❝ n=20 ❝ CV=0.18 (for Cmax and AUC) ❝ GMR1 = 0.97 ❝ GMR2=1.19 Taking into account that CV and GMR's of such a previous (pilot) trial are not the true values but estimates with uncertainty, as ElMaestro already pointed out. One answer to such a goal is using the socalled "expected power" implemented in PowerTOST::exppower.TOST() and expsampleN.TOST() .Let's play with your numbers step by step: 1. Taking into account uncertainty of CV, but assuming a known (true) GMR =0.95 expsampleN.TOST(CV=0.18, theta0=0.95, prior.parm = list(m=20, design="2x2"), prior.type="CV") Not so much more than using the conventional power assuming GMR and CV known. 2. Taking into account uncertainty of CV, but assuming a true GMR =1.19 expsampleN.TOST(CV=0.18, theta0=1.19, prior.parm = list(m=20, design="2x2"), prior.type="CV") Again slightly higher than using the conventional power. 3. Now taking into account uncertainty of CV and GMR =1.19 expsampleN.TOST(CV=0.18, theta0=1.19, prior.parm = list(m=20, design="2x2"), prior.type="both") That result (!) should everyone convince that using the GMR from pilot studies with small number of subjects (or likewise from usually small stage 1 of a TSD) is not a good idea, as our captain already stated in his post above. It results mainly from "... there is 50% chance the true GMR is worse." And power is heavily influenced by deviations in the GMR as we already know from the power analysis functions f.i. pa.ABE() BTW: Don't ask me for the theory behind expected power. It is something Bayesian. If you are interested you may find a short tractatus at https://github.com/Detlew/PowerTOST/tree/master/inst/doc written by Ben (Benjamin Lang) who is also responsible for the implementation. — Regards, Detlew 
DavidManteigas ★ Portugal, 20161228 19:11 (2461 d 04:40 ago) @ BEproff Posting: # 16899 Views: 7,897 

Hi all, Just curiosity, but does anyone here had questions on sample size from regulators? I'm asking that since in Portugal I never saw a question from regulators or ethics committees regarding sample size. They just assume we did it right 
d_labes ★★★ Berlin, Germany, 20161228 20:24 (2461 d 03:27 ago) @ DavidManteigas Posting: # 16900 Views: 7,968 

Dear David, ❝ Just curiosity, but does anyone here had questions on sample size from regulators? ❝ ❝ I'm asking that since in Portugal I never saw a question from regulators or ethics committees regarding sample size. They just assume we did it right that's also my experience. Never got a question in that direction from IEC or regulators. But got questions from sponsors, especially if the sample size doesn't fit their budget . — Regards, Detlew 
ElMaestro ★★★ Denmark, 20161228 23:22 (2461 d 00:28 ago) @ d_labes Posting: # 16901 Views: 7,953 

Dear all, ❝ (...) Never got a question in that direction from IEC or regulators. I have come across quite some members of IRBs/IECs over the past 10 years or so. I have not, however, met many who understood much about power and sample size at all. The problem is particularly pronounced at IECs where most of the members have their experience from innovator developments (or scientific literature covering this topic). Intuition can be a real prick when it is applied to the field of bioequivalence. Personally, I have had to suck up quite a few mistakes of my own which originated in intuition, trust me. — Pass or fail! ElMaestro 
mittyri ★★ Russia, 20161229 12:44 (2460 d 11:07 ago) @ DavidManteigas Posting: # 16902 Views: 7,850 

Hi David and all, ❝ Just curiosity, but does anyone here had questions on sample size from regulators? some years ago all sponsors who submitted the BEQ protocols to the Russians regulators got a question: how did you calculate the sample size? There were convinced with any answer: the code in R, values in cells in FARTSSIE, SAS code and output or even justification with some algebra. — Kind regards, Mittyri 
Helmut ★★★ Vienna, Austria, 20161229 13:39 (2460 d 10:11 ago) @ DavidManteigas Posting: # 16904 Views: 7,931 

Hi David, ❝ Just curiosity, but does anyone here had questions on sample size from regulators? Never in 35+ years (both from regulators and IECs). I share Detlew’s experience with sponsors. Too lazy to search but somewhere I posted the distribution of sample sizes of my studies. There was a sharp peak at the Babylonian 24 (two dozens were conceived a holy number). When working in a CRO at the end of the day the sponsor is always right. — 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 