ElMaestro ★★★ Denmark, 20191129 03:40 (902 d 15:00 ago) Posting: # 20893 Views: 3,050 

Hi all, I never did much work on untransformed metrics for BE calculation, but I am facing a situation where it is mandated by an authority, and what I need is the CI for the ratio (not per se the difference). I'd like to be well prepared. A relevant publication, at least for sample size, is Hauschke et al. from 1999. The ratio of two normal distributions is not itself a normal distribution. How is the calculation of the CI for the ratio actually done when the upper and lower limits are percentages of mu_{(R)} ? I think Hauschke's paper is silent on the matter, as is Chow & Liu. Note also that powerTOST's nomenclature seems to differ a bit (?) from Hauschke's in that it uses theta1 and theta2 where Hauscke would use f1 and f2. In powerTOST theta1 defaults to 0.8 when the limit for the ratio is actually 0.8*mu_{(R)}, or so I am reading it. I may be quite wrong?!? Anyhow, the important part of this post is how the CI for the ratio is actually derived. — Pass or fail! ElMaestro 
Helmut ★★★ Vienna, Austria, 20191129 10:03 (902 d 08:37 ago) @ ElMaestro Posting: # 20894 Views: 2,433 

Hi ElMaestro, » […] powerTOST's nomenclature seems to differ a bit (?) from Hauschke's in that it uses theta1 and theta2 where Hauscke would use f1 and f2. In powerTOST theta1 defaults to 0.8 when the limit for the ratio is actually 0.8*mu_{(R)}, or so I am reading it. Nope. Hauschke et al.^{1} use $$H_0:\frac{\mu_T}{\mu_R}\leqslant\theta_1\,\textrm{or}\,\frac{\mu_T}{\mu_R}\geqslant\theta_2\;\textrm{versus}\;H_1:\theta_1<\frac{\mu_T}{\mu_R}<\theta_2\tag{3}$$and \((\theta_1,\theta_2)=(0.8,1.25)\) as well (see the figures and paragraphs below them). » […] the important part of this post is how the CI for the ratio is actually derived. It never hurts to read the primary document.^{2}
— Diftor heh smusma 🖖 _{} Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 
ElMaestro ★★★ Denmark, 20191129 15:53 (902 d 02:47 ago) @ Helmut Posting: # 20897 Views: 2,383 

Hi Hötzi, » Nope. For such cases we are setting logscale to False, right?PowerTOST says about theta1: "Defaults to 0.8 if logscale=TRUE or to 0.2 if logscale=FALSE. " So as I read it, when logscale=F theta1 defaults to .2 or we set it to something negative. This is how I think Hauchke's f1 and theta1 get confused.I will read up on the other stuff. Still not sure how to derive the CI. it is a really interesting problem, though. Feels like going back into a discussion which was closed decades ago before I knew anything about BE Thanks PharmCat for the ref. below. — Pass or fail! ElMaestro 
d_labes ★★★ Berlin, Germany, 20191129 17:20 (902 d 01:20 ago) @ ElMaestro Posting: # 20898 Views: 2,467 

Dear ElMaestro, » For such cases we are setting logscale to False, right?Correct in so far if we use the approximation that the estimate of µ_{R} is (statistically) greater than zero. A very reasonable assumption for the usual metrics AUC and Cmax IMHO. But this has than nothing to do with Fieller’s (‘fiducial’) confidence interval, a more correct method for deriving a confidence interval for the ratio of untransformed PK metrics. PowerTOST has for this case the functions  CI.RatioF() Have a look into the man pages of these functions and notice that these functions don't have a logscale argument.» I will read up on the other stuff. Still not sure how to derive the CI. ... Eventually the book
may help. Especially chapter 10 Equivalence assessment for clinical endpoints. If you don't own this book, gimme a hint. — Regards, Detlew 
ElMaestro ★★★ Denmark, 20191130 05:11 (901 d 13:29 ago) @ d_labes Posting: # 20899 Views: 2,361 

Dear d_labes, you left me baffled. » » For such cases we are setting logscale to False, right?» » Correct in so far if we use the approximation that the estimate of µ_{R} is (statistically) greater than zero. A very reasonable assumption for the usual metrics AUC and Cmax IMHO. Please explain then what exactly it is that power.TOST calculates when I use logscale=F. Does it calculate power for a hypothesis based on a difference or for a ratio? Which difference? Which ratio? » But this has than nothing to do with Fieller’s (‘fiducial’) confidence interval, a more correct method for deriving a confidence interval for the ratio of untransformed PK metrics. The mention of Fieller was not mine. I am quite confused now, what it is power.TOST tries to calculate when I do logscale=F. I am convinced the assuming theta1=0.2 by default when logscale=F is a misnomer. theta1 is elsewhere understood as an equivalence margin expressed as a ratio and that can't realistically be negative. If powerTOST tries to emulate Hauschke's paper then .2 is f1, not a theta. We need to be careful here about f, delta and theta. — Pass or fail! ElMaestro 
d_labes ★★★ Berlin, Germany, 20191130 14:01 (901 d 04:39 ago) @ ElMaestro Posting: # 20900 Views: 2,317 

Dear ElMaestro, » Please explain then what exactly it is that power.TOST calculates when I use logscale=F. » Does it calculate power for a hypothesis based on a difference or for a ratio? » Which difference? Which ratio? Take the reference you mentioned above, Hauschke et al. Statist. Med 1999 and use equation (2) for the hypotheses tested, hypotheses based on the difference µTµR. These hypotheses can be reformulated with ratios as written in equation (3) by division with µR and by adding 1. But this then had the implicit constraint that µR has to be >0. » ... » I am convinced the assuming theta1=0.2 by default when logscale=F is a misnomer. theta1 is elsewhere understood as an equivalence margin expressed as a ratio and that can't realistically be negative. If powerTOST tries to emulate Hauschke's paper then .2 is f1, not a theta. PowerTOST does not emulate any paper. And it does not use the argument theta1 solely as equivalence margin of a ratio. See the man page of power.TOST() how theta1, theta2 and theta0 have to be set for logscale = TRUE or logscale=FALSE .But you are right: compared to Hauschke's paper .2 is f1. Please consider the rest of the Hauschke paper introducing the Fieller CI as exact method. — Regards, Detlew 
PharmCat ★ Russia, 20191129 12:05 (902 d 06:35 ago) @ ElMaestro Posting: # 20896 Views: 2,421 

Hello! If I correctly understand, this article can be helpful: Morisugi H, Romero J, Moriguchi T. (2009). Confidence Interval for the Ratio of Two Normal Variables (an Application to Value of Time). Interdisciplinary Information Sciences. 2019;15(1):37–43. doi:10.4036/iis.2009.37. Maybe if it will be not so hard I'll try to realized it. 