## Group effects FDA/EMA [Two-Stage / GS Designs]

Hi zizou,

sorry for reviving this old thread. See also this lengthy one.

» […] there is nothing stated from EMA on testing of such groups (or did I miss something?).

I don’t think so. I know only one case (biosimilar in 2016) where the statistician (“borrowed” from the MHRA) required the FDA’s model 2.

» I have opinion that group effect is not important in 2x2 crossover with proper planning/realization.

Agree – especially with this one.

» Moreover I have never seen group testing for EMA and I like the sentence "With one week between groups I would never ever thought a milli­second of setting up a group model." from this post.

Agree.

» I prepared example (no real data ... to get "unlucky" results).

THX! I could reproduce your results with my R-code.

» Because the data are written by me randomly and then changing and changing to get the nice and not nice results at the same. The groups separately provide us with next BE results:
»
» Group 1:
» data1=subset(data,data[,"Groups"]==1)
» CVintra = 30.9%
» PE (LL,UL) = 0.8774 (0.7554,1.0191)
»
» Group 2:
» data2=subset(data,data[,"Groups"]==2)
» CVintra = 19.4%
» PE (LL,UL) = 1.1689 (1.0626,1.2858)

Nice example. My current thinking is: The decision scheme recommended by the FDA leads to nowhere. If there is a significant G×T interaction we are not allowed to pool. So far so good. If groups are not of the same size, everybody would present the results of the largest group evaluated by the conventional model. But: What if groups have equal sizes (like in your example)? Cherry-pick and present the “better” one? I bet the assessor would ask for the other one as well. If I would be a regulator I would require that both pass or – if not – make a conservative decision: fail.
Furthermore, if there is a true G×T interaction the treatment effect might be biased. To which extent is unknown (cannot be estimated from the data). It might well be that a smaller group is closer to the true treatment effect than a larger one. We simply don’t know. IMHO, the assumption that “size matters” is false.
Your example is even more telling in another respect. Not only the PEs are different but also the variances. Should we blindly pool them? I know, the common model assumes equal variances anyhow, but…
I have just limited data (always tried to avoid equal group sizes). These are the results of my data sets which show a significant G×T interaction in model 1 and I evaluated the equally sized largest groups by model 3:

AUC drug group  n df      mse CV(%)  a     1    9  7 0.006026  7.77  a     2    9  7 0.005799  7.63  b     1   12 10 0.014851 12.23  b     2   12 10 0.006714  8.21  c     1    9  7 0.001881  4.34  c     2    9  7 0.001462  3.82 Cmax drug group  n df      mse CV(%)  b     1   12 10 0.036411 19.26  b     2   12 10 0.026247 16.31

Am I worried about different variances of groups (AUC, drug b)? Nope, p 0.6232. Drugs a & c (with extremely low CVs) were NTIDs and the PI wanted groups for safety reasons.

» If group testing was required by EMA I would prefer test for group*treatment interaction.

Disagree. What if you find one? Expected in 10% of studies by pure chance. Then you end up with the story from above.

» But I am still convinced that group effect is not required to test in standard 2x2 crossover study. For me it seems similar to have 2x2 crossover study on 24 subjects in 1 group and test for Period*Treatment interaction (e.g. in model Period*Treatment, Sequence, Subjects(Sequence) - if Period*Treatment significant, use data from one period as in parallel design.

Agree. Grizzle’s flawed testing for a sequence- (or better: unequal carryover-) effect… Cannot be handled statistically and only avoided by design.

My current thinking for the EMA (maybe I’m wrong): Avoid a potential G×T interaction by design (i.e., comply with the FDA’s conditions for pooling, keep the interval between groups short, if possible use the staggered approach).
• State in the SAP that “it is a source of variation that cannot be reasonably assumed to have an effect on the response variable”. Pool the data and evaluate the study by the common 2×2 model.
• Essentially the FDA is correct that the multigroup nature of the study should be taken into account – if (!) groups are separated by a long interval (months). If you want to go this way (remember: I have seen only a single case where the EMA asked for it), state it in the SAP. No pre-test of the G×T interaction by model 1! Pool the data and evaluate the study by the FDA’s model 2. The loss in power compared to the common 2×2 model is small.

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Helmut Schütz

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