Geometric mean and CV [Power / Sample Size]
Hi Rocco,
Oh dear, my slides always give only half of the picture (my is missing)…
Let’s start from a 2×2×2 crossover. We have the within-subject variabilities of T and R (CVwT and CVwR). Since this is not a replicate design, they are not accessible and pooled into the common CVw.1 One of the assumptions in ANOVA are identical variances. If they are not truly equal (say CVwT < CVwR) the CI is inflated: The “good” T is punished by the “bad” R.
Similar in a parallel design. You can assume that CVwT = CVwR and CVbT = CVbR and therefore, use the (pooled, total) CVp of your FIM study. Here it is the other way ’round (you have the CVp of R). If the CV of T is higher, bad luck, power compromised. If both are ~ equal, fine. If it is lower, you gain power. There is no free lunch.
If you are cautious: Pilot study or a Two-Stage-Design.2 For the latter I recommend the function
❝ […] is it even reasonable to use the geometric CV for the reference? Isn’t the CV you want to input into a sample calculation the CV corresponding to the difference of the Test and Reference? Since you do not have the Test group here, I am a bit confused as to what using the geometric CV of only the reference tells you. In your slides, you have a lone that says “if you have only mean and sd of the reference, a pilot study is unavoidable.”
❝
❝ What am I missing?
Oh dear, my slides always give only half of the picture (my is missing)…
Let’s start from a 2×2×2 crossover. We have the within-subject variabilities of T and R (CVwT and CVwR). Since this is not a replicate design, they are not accessible and pooled into the common CVw.1 One of the assumptions in ANOVA are identical variances. If they are not truly equal (say CVwT < CVwR) the CI is inflated: The “good” T is punished by the “bad” R.
Similar in a parallel design. You can assume that CVwT = CVwR and CVbT = CVbR and therefore, use the (pooled, total) CVp of your FIM study. Here it is the other way ’round (you have the CVp of R). If the CV of T is higher, bad luck, power compromised. If both are ~ equal, fine. If it is lower, you gain power. There is no free lunch.
If you are cautious: Pilot study or a Two-Stage-Design.2 For the latter I recommend the function
power.tsd.p()
of package Power2Stage
for . A reasonable stage 1 sample size is ~80% of what you estimate with sampleN.TOST(alpha=0.05...)
of package PowerTOST
and the CVp of your FIM.- Of course, the same holds for between-subject variabilities: CVbT and CVbR pooled to CVb.
- Fuglsang A. Sequential Bioequivalence Approaches for Parallel Design. AAPS J. 2014; 16(3):373–8. doi:10.1208/s1224801495711. Open access.
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Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz
The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes
Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz
The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes
Complete thread:
- Input for CV when small first-in-man has been run Rocco_M 2019-09-03 18:45 [Power / Sample Size]
- Geometric mean and CV Helmut 2019-09-05 14:05
- Geometric mean and CV Rocco_M 2019-09-05 23:49
- Geometric mean and CVHelmut 2019-09-06 00:15
- Geometric mean and CV Rocco_M 2019-09-06 17:49
- Geometric mean and CV Helmut 2019-09-06 18:15
- Geometric mean and CV ElMaestro 2019-09-06 19:03
- Geometric mean and CV Helmut 2019-09-07 09:57
- Geometric mean and CV Rocco_M 2019-09-09 13:31
- Assumptions… Helmut 2019-09-09 14:09
- Assumptions… Rocco_M 2019-09-09 16:35
- Assumptions… Helmut 2019-09-09 17:19
- Assumptions… Rocco_M 2019-09-13 21:28
- t-test & Welch-test Helmut 2019-09-14 00:16
- sampleN.TOST and CI.BE Rocco_M 2019-09-17 22:02
- assumptions vs. realizations Helmut 2019-09-18 11:04
- sampleN.TOST and CI.BE Rocco_M 2019-09-17 22:02
- t-test & Welch-test Helmut 2019-09-14 00:16
- Assumptions… Rocco_M 2019-09-13 21:28
- Assumptions… Helmut 2019-09-09 17:19
- Assumptions… Rocco_M 2019-09-09 16:35
- Assumptions… Helmut 2019-09-09 14:09
- Geometric mean and CV Rocco_M 2019-09-09 13:31
- Geometric mean and CV Helmut 2019-09-07 09:57
- Geometric mean and CV ElMaestro 2019-09-06 19:03
- Geometric mean and CV Helmut 2019-09-06 18:15
- Geometric mean and CV Rocco_M 2019-09-06 17:49
- Geometric mean and CVHelmut 2019-09-06 00:15
- Geometric mean and CV Rocco_M 2019-09-05 23:49
- Geometric mean and CV Helmut 2019-09-05 14:05