## Impact of minimum stage 2 sample size on the TIE: example [Two-Stage / GS Designs]

Hi ElMaestro,

» » Simulating for power (at 0.95):

» Should this not be

Nope. This is the original Potvin ‘Method B’. There is no minimum

» Do you think you have it in your heart to explain in slow motion to a dimwit like me who read your posts quite a few times which point you are trying to prove or investigating?

I’ll try.

If we mandate

That’s pure reasoning (wetware).

» Otherwise I am afraid I will need to question you next time we meet f2f. And that might not be in the distant future

Really? Great!

» » Simulating for power (at 0.95):

- No lower limit of
*n*_{2}

» »`library(Power2Stage)`

» »`power.2stage(method="B", alpha=rep(0.0294, 2), CV=0.2,`

» »`n1=12, GMR=0.95, targetpower=0.8, min.n2=0)`

» Should this not be

`min.n2=2`

?Nope. This is the original Potvin ‘Method B’. There is no minimum

*n*_{2}in the paper, right? However, the functions in`Power2Stage`

are constructed in such a way that (in cross-over TSDs) any estimated sample size has to be an even number. If one states `min.n2=1`

it will automatically rounded up to 2. Same goes with `sampleN.TOST()`

. Hence, to state `min.n2=2`

is a waste of time (see also the footnote to this post).» Do you think you have it in your heart to explain in slow motion to a dimwit like me who read your posts quite a few times which point you are trying to prove or investigating?

I’ll try.

*Without*a minimum*n*_{2}what would happen in a study which – following the conditions of the framework – could proceed to the second stage?*n*_{2}could be*any*even number. Say we had*n*_{1}24 and estimate the total sample size*N*(for stage 1 CV, assumed GMR and target power) with 30. Hence,*n*_{2}6.If we mandate

*n*_{2}= max(*n*_{2}= 1.5*n*_{1},*N*–*n*_{1}) we have to perform the second stage in 36 instead of 6. In the pooled analysis we will have 60 subjects instead of 30. Much higher power (nice for wealthy sponsors) but not so nice if we look at the TIE. Since the final size is twice as large, the chance to pass BE (at 1.25) will be larger as well. Even if we keep everything equal the DFs come into play. Therefore, the ‘original’ adjusted α might not sufficiently control the TIE – and we would need a*lower*one.That’s pure reasoning (wetware).

» Otherwise I am afraid I will need to question you next time we meet f2f. And that might not be in the distant future

Really? Great!

—

Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮

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*Dif-tor heh smusma*🖖Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮

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### Complete thread:

- Impact of minimum stage 2 sample size on the Type I Error Helmut 2016-12-30 01:22
- Impact of minimum stage 2 sample size on the Type I Error ElMaestro 2016-12-30 12:12
- Impact of minimum stage 2 sample size on the TIE: example Helmut 2016-12-30 14:01
- Impact of minimum stage 2 sample size on the TIE: example ElMaestro 2016-12-30 17:19
- Impact of minimum stage 2 sample size on the TIE: exampleHelmut 2016-12-30 18:00
- Impact of minimum stage 2 sample size on the TIE: example ElMaestro 2016-12-30 18:50
- Bingo! Helmut 2016-12-30 19:00

- Impact of minimum stage 2 sample size on the TIE: example ElMaestro 2016-12-30 18:50

- Impact of minimum stage 2 sample size on the TIE: exampleHelmut 2016-12-30 18:00

- Impact of minimum stage 2 sample size on the TIE: example ElMaestro 2016-12-30 17:19

- Impact of minimum stage 2 sample size on the TIE: example Helmut 2016-12-30 14:01

- Impact of minimum stage 2 sample size on the Type I Error ElMaestro 2016-12-30 12:12