Now what? w & w* examples [Two-Stage / GS Designs]
Dear Helmut,
I have tried to demystify some aspects of choosing w and w* for the maximum combination test by looking into some examples:
Take nfix as sample size in stage 1 (Helmut’s proposal)
Guess:
CV=0.2, theta0=0.95 -> nfix = 20
Choose n1 = nfix = 20, i.e. w= 0.99, since w has to be <1.
Guess was too pessimistic:
e.g. true CV=0.15 -> nfix = 12
or theta0=0.975 -> nfix = 16
For both the sample size for stage 1 exceed the necessary total sample size of a fixed design. Thus a more realistic w* can’t be defined or should be set to the same value as w.
This results in the standard combination test.
Guess was too optimistic:
e.g. true CV=0.25 -> nfix = 28
or theta0=0.925 -> nfix = 26
Both lead to a ‘more realistic’ w*= 0.71 or 0.77. Let's choose w* = 0.7 for simplicity.
Power & sample size of the scenarios
Take nfix/2 as sample size in stage 1 (Maurer et al.)
Guess:
CV=0.2, theta0=0.95 -> nfix = 20
Choose n1 = nfix/2 = 10, i.e. w= 0.5.
Guess was too pessimistic:
e.g. true CV=0.15 -> nfix = 12
or theta0=0.975 -> nfix = 16
This would let to a ‘more realistic’ w*= 0.83 or 0.625, respectively. Let's take for simplicity w* = 0.7.
Guess was too optimistic:
e.g. true CV=0.25 -> nfix = 28
or theta0=0.925 -> nfix = 26
Both lead to a ‘more realistic’ w*= 0.36 or 0.38. Let's take for simplicity w* = 0.4.
Power & sample size of the scenarios
Confusion :
I have tried to demystify some aspects of choosing w and w* for the maximum combination test by looking into some examples:
Take nfix as sample size in stage 1 (Helmut’s proposal)
Guess:
CV=0.2, theta0=0.95 -> nfix = 20
Choose n1 = nfix = 20, i.e. w= 0.99, since w has to be <1.
Guess was too pessimistic:
e.g. true CV=0.15 -> nfix = 12
or theta0=0.975 -> nfix = 16
For both the sample size for stage 1 exceed the necessary total sample size of a fixed design. Thus a more realistic w* can’t be defined or should be set to the same value as w.
This results in the standard combination test.
Guess was too optimistic:
e.g. true CV=0.25 -> nfix = 28
or theta0=0.925 -> nfix = 26
Both lead to a ‘more realistic’ w*= 0.71 or 0.77. Let's choose w* = 0.7 for simplicity.
Power & sample size of the scenarios
N
------------------
CV theta0 w w* power ASN Median p95%
-------------------------------------------------------------------------
Guess 0.20 0.95 0.99 0.5*) 0.866 21.5 20 34
0.99 0.99 0.872 24.9 20 30
0.99 0.7 0.870 21.5 20 28
Too pessimistic 0.15 0.95 0.99 0.99 0.966 20.1 20 20
0.20 0.975 0.99 0.99 0.936 22.9 20 24
Too optimistic 0.25 0.95 0.99 0.7 0.842 29.1 20 64
0.20 0.925 0.99 0.7 0.760 22.6 20 36
-------------------------------------------------------------------------
*) w* = w/2 according to Maurer et al.
No futility criterion
Take nfix/2 as sample size in stage 1 (Maurer et al.)
Guess:
CV=0.2, theta0=0.95 -> nfix = 20
Choose n1 = nfix/2 = 10, i.e. w= 0.5.
Guess was too pessimistic:
e.g. true CV=0.15 -> nfix = 12
or theta0=0.975 -> nfix = 16
This would let to a ‘more realistic’ w*= 0.83 or 0.625, respectively. Let's take for simplicity w* = 0.7.
Guess was too optimistic:
e.g. true CV=0.25 -> nfix = 28
or theta0=0.925 -> nfix = 26
Both lead to a ‘more realistic’ w*= 0.36 or 0.38. Let's take for simplicity w* = 0.4.
Power & sample size of the scenarios
N
------------------
CV theta0 w w* power ASN Median p95%
-------------------------------------------------------------------------
Guess 0.20 0.95 0.5 0.25*) 0.838 22.7 20 46
0.5 0.7 0.844 22.6 18 50
0.5 0.4 0.841 22.5 20 48
Too pessimistic 0.15 0.95 0.5 0.7 0.881 13.0 10 24
0.20 0.975 0.5 0.7 0.896 21.4 18 48
Too optimistic 0.25 0.95 0.5 0.4 0.822 37.1 34 78
0.20 0.925 0.99 0.4 0.747 24.1 20 52
-------------------------------------------------------------------------
*) w* = w/2 according to Maurer et al.
No futility criterion
Confusion :
- Different weights w* don’t make a big difference, I think
- Too pessimistic specifications result in higher power and lower expected sample size (!) (at least for CVs around 0.2)
- Too optimistic specifications may result in lower power and higher expected sample size (!)
- Choosing the sample size for stage 1 as the sample size of a fixed design seems to have some advantages w.r.t. power and expected sample size compared to the 'midterm' sample size re-estimation. Except the too pessimistic settings row CV=0.15, theta0=0.95 ... for the 'midterm' SSR.
—
Regards,
Detlew
Regards,
Detlew
Complete thread:
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- Binding / Nonbinding futility rule - alpha control d_labes 2018-06-16 19:42
- Nonbinding futility rule Ben 2018-06-15 17:58
- Nonbinding futility rule d_labes 2018-06-14 10:47
- Bad weather? Helmut 2018-06-13 19:23
- Nonbinding futility rule d_labes 2018-06-13 16:59
- a bug in interim.tsd.in()? Ben 2018-06-12 19:32
- Now what? w & w* examples Helmut 2018-06-11 13:57
- Now what? w & w* examples Ben 2018-06-10 20:12
- Selection of w and w* Helmut 2018-04-26 09:51