## Some answers [Two-Stage / GS Designs]

Hi Mittyri,

I’m in a hurry; so answering only part of your questions (leave the others to Detlew or Ben).

» 2. Why did you decide to include CI futility rule by default?

This applies only to the x.tsd.in functions (to be in accordance with the paper of Maurer et al.).

» isn't it possible that we get some value lower than 4?
» for example and after first stage CV=15%, CI=[0.7991897 1.0361745]:
» sampleN2.TOST(CV=0.15, n1=12) »  Design  alpha   CV theta0 theta1 theta2 n1 Sample size »     2x2 0.0294 0.15   0.95    0.8   1.25 12           2

sampleN2.TOST() is intended for the other methods where at the end stages are pooled.
In the inverse normal method stages are evaluated separately (PE and MSE from ANOVAS of each stage). If you have less than 4 subjects in the second stage you will run out of steam (too low degrees of freedom). Well, 3 would work, but…

» 5. I was confused with "2stage" 'aliased' with "tsd" and was looking for differences some time
» Are there any reasons to double that functions?

Since this is a 0.x-release according to CRAN’s policy we can rename functions or even remove them without further notice. We decided to unify the function-names. In order not to break existing code we introduced the aliases. In the next release functions x.2stage.x() will be removed and only their counterparts x.tsd.x() kept.

» regarding 3rd point:
» I tried
» interim.tsd.in(GMR1=sqrt(0.7991897 * 1.0361745), CV1=0.15,n1=12, fCrit="No")» […] » - Calculated n2 = 4» - Decision: Continue to stage 2 with 4 subjects
» oh, there's a default argument min.n2 = 4
» OK, let's try to change that:
» interim.tsd.in(GMR1=sqrt(0.7991897 * 1.0361745), CV1=0.15,n1=12, fCrit="No", min.n2 = 2)» Error in interim.tsd.in(GMR1 = sqrt(0.7991897 * 1.0361745), CV1 = 0.15,  : »   min.n2 has to be at least 4.
» Why couldn't I select a smaller one?

See above. Doesn’t make sense with zero degrees of freedom (n2=2).

Dif-tor heh smusma 🖖
Helmut Schütz

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