Elena777 ☆ Belarus, 20180718 15:35 Posting: # 19079 Views: 1,848 

Dear colleagues. I would like to know how pharamcokinetic data is manipulated during statistical analysis. Specifically, I would like to know when we should perform ln transformation: we calculate Geom mean and then transform its value to ln value and do ANOVA or we transform each concentration Cmax to Lndata and then calculate geom mean. Or maybe it is not relevant? I also would like to know how GMR is usually presented in tables. Whether it is usually GMR of lntransformed or nontransformed data? I provide the table below as an example and would like to know the origin of its values as I asked above. 
ElMaestro ★★★ Denmark, 20180718 23:19 @ Elena777 Posting: # 19080 Views: 1,686 

Hi Elena777, I did not sanitycheck the figures but at a glance it looks all normal and consistant with a standard BE study outcome. Re. "manipulations": Data is typically logtransformed and then fit with a normal linear model with the four standard factors. If you use WNL with default settings it will fit Subject as random. From the model fit comes a set of effects for the factors, which give rise to treatment LSMeans (on the log scale). These are then backtransformed to the normal scale. — if (3) 4 Best regards, ElMaestro “(...) targeted cancer therapies will benefit fewer than 2 percent of the cancer patients they’re aimed at. That reality is often lost on consumers, who are being fed a steady diet of winning anecdotes about miracle cures.” New York Times (ed.), June 9, 2018. 
Ohlbe ★★★ France, 20180719 10:31 @ Elena777 Posting: # 19081 Views: 1,657 

Dear Elena, I am not a statistician, but I should be able to answer in lay language » Specifically, I would like to know when we should perform ln transformation: we calculate Geom mean and then transform its value to ln value and do ANOVA or we transform each concentration Cmax to Lndata and then calculate geom mean. Logtransform each value and then calculate the arithmetic (not geometric) mean of the logtransformed values. If you calculate the exponential of this arithmetic mean, it will give you the geometric mean on the original scale. » I also would like to know how GMR is usually presented in tables. Whether it is usually GMR of lntransformed or nontransformed data? I provide the table below as an example and would like to know the origin of its values as I asked above. What you have in your table is the nontransformed data. Just try and calculate the exponential: the values you would get for AUC would be really extreme. If reporting logtransformed data: you would need to calculate a difference, not a ratio. The ratio in your table is not a geometric mean ratio and is therefore rightly not labelled GMR. It is a ratio of LSMEAN, not of geometric means. It makes no difference if your study is balanced (i.e. same number of subjects with sequence TR and sequence RT, in which case the LSMEAN is equal to the geometric mean), but if you have a dropout and your study gets unbalanced you will get some differences. — Regards Ohlbe 
Helmut ★★★ Vienna, Austria, 20180719 13:01 @ Ohlbe Posting: # 19083 Views: 1,638 

Hi Elena, extending what Ohlbe wrote… » The ratio in your table is not a geometric mean ratio and is therefore rightly not labelled GMR. It is a ratio of LSMEAN, not of geometric means. It makes no difference if your study is balanced (i.e. same number of subjects with sequence TR and sequence RT, in which case the LSMEAN is equal to the geometric mean), but if you have a dropout and your study gets unbalanced you will get some differences. See this post for an example of unbalanced sequences and especially the PS. — Cheers, Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. ☼ Science Quotes 
crdreyes ☆ Philippines, 20180725 17:21 @ Helmut Posting: # 19101 Views: 1,402 

» » The ratio in your table is not a geometric mean ratio and is therefore rightly not labelled GMR. It is a ratio of LSMEAN, not of geometric means. It makes no difference if your study is balanced (i.e. same number of subjects with sequence TR and sequence RT, in which case the LSMEAN is equal to the geometric mean), but if you have a dropout and your study gets unbalanced you will get some differences. » » See this post for an example of unbalanced sequences and especially the PS. Hi Helmut, From your experience, do you recommend showing the geometric means along with the adjusted geometric means (from ls means) and the ratio from the adjusted in a single table? Or it does not make sense to even include the geometric means. My initial thought it would be good to include both just to see if the adjustments from the model. But it may cause confusion and may not be worth it. Thanks, Russel 
Helmut ★★★ Vienna, Austria, 20180726 15:38 @ crdreyes Posting: # 19106 Views: 1,385 

Hi Russel, » […] do you recommend showing the geometric means along with the adjusted geometric means (from ls means) and the ratio from the adjusted in a single table? Or it does not make sense to even include the geometric means. My initial thought it would be good to include both just to see if the adjustments from the model. But it may cause confusion and may not be worth it. Well, that’s a matter of taste. I mainly deal with European submissions where the tables mentioned in Appendix IV of the BEGL are mandatory (see esp. Table 3.1). Never give the arithmetic means (we know that AUC and C_{max} follow a lognormal distribution) but the geometric means ±CV%. I tried to convince the EMA to use only the geometric least squares means (see there) but didn’t succeed. Geometric means are fine to represent the outcome of subjects under each treatment. In the synopsis of my statistical reports I always give x_{geo} ± CV%. Only if the study was unbalanced (or a parallel design with unequal group sizes) I give additionally the GLSM ± SD (together with a footnote clarifying why they are different). That’s like you would do. Never got a request for clarification from any agency (either the assessors were clever or the footnote helped). — Cheers, Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. ☼ Science Quotes 