Advantages – performance measure [Two-Stage / GS Designs]

posted by d_labes  – Berlin, Germany, 2015-06-05 23:27 (3241 d 12:52 ago) – Posting: # 14920
Views: 17,959

Dear Helmut!

❝ Tricky. Essentially they compare adaptive designs in terms of the expected total sample and power to a fixed sample design. They call the latter “ideal” which is only true for a known CV. Such a comparison is not fair but the only one we probably have. ;-)


TXH for educating me.
But I wonder why a not fair comparision should guide me. May it be it is the only we have or not. And a known CV would answer all our kwestiones. Even 42.

❝ Below some stuff (the adjusted HP is 0.0413, not 0.416 – which is for OF).


I expected that nitpicking :-D. Mine was from a sparser grid than that what we know now. But was too lazy to recalculate (Remember the Mahatma story?). IMHO this makes no practical difference.

❝ ... f1 is the ratio of sample sizes and f2 the ratio of expected power:


Means to me lower f1 is preferrable, higher f2 also. But you can arrive at higher power with higher than necessary sample size. I think this is meant with "overshot". And this isn't preferrable.

❝ ... How to calculate their APS (average performance score) combining the sample size and power is beyond me.


I will have a look next time. May be Buddha is enlighten me :cool:.

❝ ... The winner is the symmetric split for my personal favorite n1 ~0.75 of fixed.


I can't follow you here.

May it be that not only old beliefs die hard but also old Vorurteile (prejudices) :-D?

I don't see an overall better performance of the symmetrical split w.r.t to your explained f1 and f2 with a fixed n1. Sometimes f1 points to preference of Pocock adj., sometimes to preference for HP or OBF (adj. of course to control TIE), sometimes f2 in the same direction or vice versa.
And the n1 ~0.75*n of fixed rule is not applicable if you bear in mind that the preposition of my post was considerably uncertainty about the intra-subject variability or CV.

Of course, if you have some really good guesstimate of the CV your preferred rule is preferred from the preferred (aka initiated) :cool:.

BTW: How did you arrive at the histograms of the sample size?
Would it be helpful to have an option to obtain the data for such graphics from the functions in Power2Stage?
—
Regards,

Detlew

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