Mutasim ☆ Jordan, 20190807 13:16 Posting: # 20484 Views: 822 

Dear Members, What is the explanation for and the impact of having a significant group by sequence interaction in a study with 4 groups and 2 sequences and 4 periods (HVD). 
Helmut ★★★ Vienna, Austria, 20190807 14:35 @ Mutasim Posting: # 20485 Views: 710 

Salam Mutasim, » What is the explanation for … Chance. » … and the impact of having a significant group by sequence interaction in a study with 4 groups and 2 sequences and 4 periods (HVD). If you are dealing with ABEL (the EMA’s method) chances than any (!) European agency will ask for a groupterm in the analysis are close to nil. When I gave my first presentation about it in Budapest 2017 in front of European regulatory (!) statisticians they were asking in disbelieve “What the hell are you talking about?” See what the BEGL states: The study should be designed in such a way that the formulation effect can be distinguished from other effects. This is in line what is stated in the Q&A document about TwoStage Designs: A model which also includes a term for a formulation*stage interaction would give equal weight to the two stages, even if the number of subjects in each stage is very different. The results can be very misleading hence such a model is not considered acceptable. […] this model assumes that the formulation effect is truly different in each stage. If such an assumption were true there is no single formulation effect that can be applied to the general population, and the estimate from the study has no real meaning. Replace formulation*stage by formulation*group and you get the idea.But all of this is about a groupbytreatment interaction. Sometimes in conventional 2×2×2 crossovers we see a significant sequence (better unequal carryover) effect. Since it can only be avoided by design (sufficiently long washout) any test for it is futile. Check for eventual residual concentrations in higher period(s) and exclude subjects with predose concentrations >5% of their C_{max}values. In analogy the same can be said about the groupbysequence interaction. Occasionally you will see a significant result. Ignore it. Only if you prefer braces plus suspenders: Go with the FDA’s model II. But no pretest and no interaction! The loss in power as compared to the pooled analysis is negligible. Degrees of freedom in a 2×2×2 design: Model III: n_{1} + n_{2} – 2 Model III: 3(n_{1} + n_{2}) – 4 What you never ever should do: Evaluate groups separately. Power will be terrible. Even if you are extremely lucky and one of them passes, what will you do? Submit only this one? Any agency will ask for the others as well. Guess the outcome. If you are thinking about the FDA’s group models I–III: There were very few deficiency letters (all with the same text) issued to US and Canadian companies. On the other hand, many European CROs have such letters collecting dust in archives. Politics? Even if you follow this track, model III (pooling) is justified since the conditions are practically always fulfilled. The sequential procedure (test for a groupbytreatment interaction at the 10% level) as any pretest might inflate the type I error. I even have a statement from the EMA that such a procedure is not acceptable. Groupbysequence interaction? Forget it.
— Cheers, Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 
ElMaestro ★★★ Belgium?, 20190808 10:06 @ Helmut Posting: # 20486 Views: 650 

Hi Hötzi, » The precise model to be used for the analysis should be prespecified in the protocol. The statistical analysis should take into account sources of variation that can be reasonably assumed to have an effect on the response variable. » Is it reasonable to assume that groups or sequences have an effect on the PK response? Heck, no way! Same inclusion/exclusioncriteria and hence, similar demographics, same clinical site, same bioanalytical method. Hundreds (thousands?) of studies performed in multiple groups and accepted by European agencies based on pooled data. But on the other hand: Why then then include e.g. period. Let us for a moment disregard the actual wording. I don 't think this is about assuming that some factor has or hasn't an effect (and not about significance in the statistical sense either). As I see it I want to construct a CI which is as wide as my real uncertainty dictates. I start out with a whole bunch of ugly variation and in the fashion of Michelangelo working on his crude blocks of marble I chip parts and bits away from my bulk of variation by applying my model. What I have left of my variation is a chunk of pristine, genuine, holy, magnificent, beautiful variation. What a sight to behold , and whose origin my experimental setup cannot account for, which I therefore use for my CI. My confidence interval now is as wide as just exactly that uncertainty merits. For a crossover this is of little practical importance since subjects are in groups. For a parallel trial I think I want group in the model. If regulators don't like this, they can ask me to take it away. I happily do so without protesting. I am a sheep at that point. But not until then. — I could be wrong, but... Best regards, ElMaestro 
Helmut ★★★ Vienna, Austria, 20190808 10:31 @ ElMaestro Posting: # 20487 Views: 647 

Ahoy, my Capt’n, » » The precise model [] » » But on the other hand: Why then then include e.g. period. Cause otherwise eventual period effects would not mean out. Given, sometimes one has to assume lacking period effects and everybody is happy with that. If an originator explores whether the drug follows linear PK, we have a paired design (SD → saturation → steady state) and compare AUC_{0–τ} with AUC_{0–∞}. A crossover would be a logistic nightmare. » Let us for a moment disregard the actual wording. [ Exactly. » […] For a parallel trial I think I want group in the model. If regulators don't like this, they can ask me to take it away. I happily do so without protesting. I am a sheep at that point. But not until then. Agree again. — Cheers, Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 