Helmut ★★★ Vienna, Austria, 20191016 13:14 Posting: # 20690 Views: 1,885 

Dear all, sometimes I see reports with a strange pattern of missing periods, e.g., subjects without the 1^{st} period, subjects with only the 1^{st} and the 4^{th}, etc. Since I have only the statistical part of the report, I have no clue what was going on. For the FDA’s RSABE only subjects with complete data (all periods) are used. But this is not the case for the EMA’s ABEL (all subjects with at least one T and R treatment for the calculation of the CI, all subjects with two R treatments for the calculation of CV_{wR}). I would say:
I saw even cases where a subject in sequence TRTR had data only of periods 1 & 3. Useless. Another case: 44 subjects had two administrations of T and R. However, 15 had two administrations of T and 39 two administrations of R. Chance – or what? — Cheers, Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 
ElMaestro ★★★ Belgium?, 20191016 15:10 @ Helmut Posting: # 20693 Views: 1,757 

Hi Hötzi, » I would say:
Not a definitive answer, but at some CROs they have SOPs in place to the effect of allowing subjects to come back after they have skipped a period , or fractions of one. This, I think, relates originally to FDA's data driven policies. A subject can walkout on her/his own initiative without even stating a reason, that's how GCP works. There is no clause saying she/he can't come back. I know, this is messy, but that's the way it is in some clinics. The alternative may be worse, depending on how you look at it: If a subject misses an ambulatory pksample ("I forgot", "I sat stuck in a traffic jam", "My parrot suffered an anxiety attack", "Don't you fucking ask me what I did yesterday, it's none of your business", "I had to watch Conchita Wurst win the Grand Prix" etc.) should she/he then be considered completely out? — Le tits now. Best regards, ElMaestro 
Helmut ★★★ Vienna, Austria, 20191017 10:22 @ ElMaestro Posting: # 20700 Views: 1,720 

Hi ElMaestro, » This, I think, relates originally to FDA's data driven policies. A subject can walkout on her/his own initiative without even stating a reason, that's how GCP works. There is no clause saying she/he can't come back. » I know, this is messy, but that's the way it is in some clinics. I see. Since the data are useless for the FDA’s referencescaling maybe this was a trickshot of the CRO to milk the sponsor. » The alternative may be worse, depending on how you look at it: If a subject misses an ambulatory pksample […] should she/he then be considered completely out? I like your examples! That’s an area of improvement which requires a good amount of intellectual horsepower. Not easy but the idea is to come up with rules specifying how many missings (and where) in the profile will likely lead to unreliable estimates of a given PK metric. — Cheers, Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 
d_labes ★★★ Berlin, Germany, 20191017 17:22 @ Helmut Posting: # 20701 Views: 1,676 

Dear Helmut! » sometimes I see reports with a strange pattern of missing periods, e.g., subjects without the 1^{st} period, subjects with only the 1^{st} and the 4^{th}, etc. ... » Did you see cases like this? And if yes, do you know why? Yes, I saw such cases in times I was active. F.i. a subject refuses consent after the 2nd period but came back due to invention of the investigator (by phone) for the 4th period (arguments: "you will not get payed if you refuse, only if you come back" or "Please, please be so kind to continue because otherwise we have not the scientific success we expected" and so on. See ElMaestros post for other examples.) What to do with such data? DUNO exactly. You yourself have described what to do for EMA ABEL or FDA RSABE. Any case left? — Regards, Detlew 
ElMaestro ★★★ Belgium?, 20191017 20:17 @ d_labes Posting: # 20702 Views: 1,664 

Hi d_labes, » "you will not get payed if you refuse, only if you come back" » "Please, please be so kind to continue because otherwise we have not the scientific success we expected" §4.8.3: Neither the investigator, nor the trial staff, should coerce or unduly influence a subject to participate or to continue to participate in a trial. — Le tits now. Best regards, ElMaestro 
d_labes ★★★ Berlin, Germany, 20191018 11:48 @ ElMaestro Posting: # 20703 Views: 1,639 

Dear ElMaestro, » ... » §4.8.3: Neither the investigator, nor the trial staff, should coerce or unduly influence a subject to participate or to continue to participate in a trial. Was of course not an unduly influence. Only a friendly talk by phone to obtain some informations about the reasons and circumstances of refusing the continuation. — Regards, Detlew 
Helmut ★★★ Vienna, Austria, 20191019 14:30 @ d_labes Posting: # 20706 Views: 1,572 

Dear Detlew, » You yourself have described what to do for EMA ABEL or FDA RSABE. Yep. » Any case left? Nope. I was just wondering what could be the cause of such results. — Cheers, Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 
Astea ★ Russia, 20191020 14:46 @ Helmut Posting: # 20709 Views: 1,558 

Dear smart people! I've got two questions on this topic, that could be logically combined into the one philosophical: should we try to keep as much data as possible for the analysis? 1). How would you advice to deal with subjects, who have only one (2,3..) points over LLOQ in one of the periods? According to EMA it is possible to exclude subjects with AUC of reference product less than 5% of geometric mean AUC. Should we exclude all the data of such a subject or can we remain data from other periods? Example: acetylcalycic acid (ASA) in entericcoated form has a widerange T_{max} in about 2 to 7 hours with extremely rapid conversion to salycic acid. So PK profiles look like "zerozerovertical linezerozero"... For catching it one has to plan a great number of sample points and use appropriate stabilization procedure. Even in these case there could be the uppermentioned problems. 2). What was the real reason for FDA to develop an algorithm for NTIDs with only complete data? As Helmut mentioned in the post, theoretically it is possible to use all the data even with incomplete data. Why then FDA just throw data of subjects with incomplete data to the bin? Is not it unethical? (I can't understand this point) — "Being in minority, even a minority of one, did not make you mad" 
Helmut ★★★ Vienna, Austria, 20191020 15:23 @ Astea Posting: # 20710 Views: 1,571 

Hi Nastia, » Dear smart people! » should we try to keep as much data as possible for the analysis? In principle yes – as long as the outcome is meaningful. » 1). How would you advice to deal with subjects, who have only one (2,3..) points over LLOQ in one of the periods? […] Tricky – IMHO, case by case (should be laid down in an SOP or the SAP, of course). IIRC, Health Canada had a rule that 1 (one!) concentration is sufficient for C_{max} and 3 (oh dear!) fo AUC. Gone with the wind. THX, HC. » 2). What was the real reason for FDA to develop an algorithm for NTIDs with only complete data? No idea. The same is applicable to all RSABEmethods of the FDA. » […] theoretically it is possible to use all the data even with incomplete data. Sure. » Why then FDA just throw data of subjects with incomplete data to the bin? Again – no idea. » Is not it unethical? (I can't understand this point) IMHO, it is and we are not alone with this conclusion.* If SABE is applied, subjects with one missing R observation should be eliminated […]. This is unprecedented in our experience in a regulated bioequivalence setting. Traditionally, one does not exclude data unless there is a scientifically or clinically valid reason to do so. However, with the current draft guidance from FDA for progesterone bioequivalence, this appears to be the immediate approach to be applied for SABE.
— Cheers, Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 
Astea ★ Russia, 20191021 01:59 @ Helmut Posting: # 20711 Views: 1,536 

Dear Helmut and other smart people! » Are you talking to me? Hey, you! Yes, you! » Tricky – IMHO, case by case (should be laid down in an SOP or the SAP, of course). IIRC, Health Canada had a rule that 1 (one!) concentration is sufficient for C_{max} and 3 (oh dear!) fo AUC. Gone with the wind. THX, HC. Suppose we have only one measurable concentration and it is as high as C_{max} of other periods. Even in this case formally we can calculate AUC for linear trapezoidal rule (depending on the rule for BLOQ data of course). And it can be more than 5 percent of geometric mean of other AUC in this period. Leave it or waste it  any choice will be wrong 
Helmut ★★★ Vienna, Austria, 20191021 11:37 @ Astea Posting: # 20712 Views: 1,486 

Dear Nastia, » Suppose we have only one measurable concentration and it is as high as C_{max} of other periods. Even in this case formally we can calculate AUC for linear trapezoidal rule […]. And it can be more than 5 percent of geometric mean of other AUC in this period. Leave it or waste it  any choice will be wrong Well, that’s why I wrote above » » […] as long as the outcome is meaningful. We have to go out on a limb to call a triangle a “curve”. — Cheers, Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 
Mahmoud ☆ Jordan, 20191106 11:41 @ Helmut Posting: # 20753 Views: 1,088 

Dear all ===== For missing data in Be studies in certain period if you need to take into account the missing data use proc mixed in SAS with dffm=Kr otherwise use proc glm in SAS, this procedure do not take into account the missing data 
Helmut ★★★ Vienna, Austria, 20191106 12:49 @ Mahmoud Posting: # 20754 Views: 1,088 

Hi Mahmoud, » if you need to take into account the missing data use proc mixed in SAS with dffm=Kr From a theoretical perspective, maybe. The KenwardRoger approximation recovers more information from the data, higher degrees of freedom and hence, results in a narrower confidence interval than with Satterthwaite’s degrees of freedom. Since the latter is explicitly recommended in the progesterone guidance I have some doubts whether the FDA would accept that. » otherwise use proc glm in SAS, this procedure do not take into account the missing data Sure. — Cheers, Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 
PharmCat ★ Russia, 20191106 14:43 @ Helmut Posting: # 20755 Views: 1,085 

» From a theoretical perspective, maybe. The KenwardRoger approximation recovers more information from the data, higher degrees of freedom and hence, results in a narrower confidence interval than with Satterthwaite’s degrees of freedom. Since the latter is explicitely recommended in the progesterone guidance I have some doubts whether the FDA would accept that. I thought that KenwardRoger provide the same DF as Satterthwaite's for onedimension effects, so as CI for coefficient is onedimension hypothesis DF should be the same, as it describes in reference paper, may be SAS make any corrections, I don't know... Is any comparations anywhere? 
Helmut ★★★ Vienna, Austria, 20191108 20:57 @ PharmCat Posting: # 20771 Views: 1,025 

Hi PharmCat, » I thought that KenwardRoger provide the same DF as Satterthwaite's for onedimension effects, so as CI for coefficient is onedimension hypothesis DF should be the same, as it describes in reference paper, may be SAS make any corrections, I don't know... Try this one:
The EMA’s Method B evaluated by lmer() of package lmerTest . KenwardRoger’s degrees of freedom ≥ Satterthwaite’s.— Cheers, Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 
PharmCat ★ Russia, 20191108 22:08 @ Helmut Posting: # 20773 Views: 1,017 

» The EMA’s Method B evaluated by lmer() of package lmerTest . KenwardRoger’s degrees of freedom ≥ Satterthwaite’s.May be it's specific of realization in lmer. I can't understand what's under the hood I will make test in SAS. 
mittyri ★★ Russia, 20191108 22:59 @ PharmCat Posting: # 20774 Views: 1,003 

Hi PharmCat, » I thought that KenwardRoger provide the same DF as Satterthwaite's for onedimension effects, so as CI for coefficient is onedimension hypothesis DF should be the same, as it describes in reference paper, may be SAS make any corrections, I don't know... "KR modify the statistic F to improve the small sample properties by approximating the distribution of F by an F_{d,m} distribution, and they also provide a method for calculating the denominator degrees of freedom m. The fundamental idea is to calculate the approximate mean and variance of their statistic and then match moments with an F distribution to obtain the denominator degrees of freedom." from here » Is any comparations anywhere? yes, plenty of. For example here Also citing Kuznetsova et al.: "From our practice, we observed that the p values that the approximation methods provide are generally very close to each other. Schaalje, McBride, and Fellingham (2002) performed a number of simulations in order to investigate the appropriateness of the approximation methods. They discovered that complexity of the covariance structures, sample size and imbalance affect the performance of both approximations. However, these factors affect the Satterthwaite’s method more than the KenwardRoger’s." — Kind regards, Mittyri 
PharmCat ★ Russia, 20191109 00:51 @ mittyri Posting: # 20775 Views: 993 

I look this or here. "Lemma 7.2.1 When l = 1, the Satterthwaite, the KR and the proposed methods give the same estimate of the denominator degrees of freedom" p.98 proof in source. For coefficient estimate l = 1, and thou I thought they should be equal (and the really equal in SAS). But when DDFM=KENWARDROGER is set  variancecovariance matrix of the fixed effects is corrected too and resulting CI would be differ  this I didn’t take it into account. 
Helmut ★★★ Vienna, Austria, 20191109 16:22 @ mittyri Posting: # 20778 Views: 1,038 

Hi mittyri and all other nerds, a rant from Douglas Bates (maintainer of lme4 ) reproduced in all of its beauty:Users are often surprised and alarmed that the summary of a linear mixed model fit by — Cheers, Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 
PharmCat ★ Russia, 20191109 19:11 @ Helmut Posting: # 20779 Views: 925 

» Hi mittyri and all other nerds, Hi all again from this part holy war began usually I really love how Douglas writes and this is one more of his explanation. And I fully support this point of view and concept of statistical purity (lme4, MixedModels.jl ets): you have modeling results and then do what you want. Problem is that we have real "degrees of freedom police" I think that more compromised and more conservative is a "contain" DF = N  rank(XZ), but FDA wrote Satterthwaite. 