## Geometric mean and CV [Power / Sample Size]

Hi Rocco,

» So basically your analysis follows from the fact that the variance of the difference of T and R equal the sum of the variance of T and the variance of R, correct?

Well, you have four variance components (s²wR, s²wT, s²bT, s²bR). Then
1. Full replicate designs
All are identifiable.
2. 2×2×2 crossover (balanced and complete for simplicity – otherwise, weighting is required)
s²w = (s²wR + s²wT)/2 and s²b = (s²bT + s²bR)/2.
3. 2 group parallel
Only the pooled (total) s²p. With a tricky mixed-effects model you could get s²pT and s²pR.
4. One treatment (FIM)
Only s²p.
Hence, if you want to plan #3 based on #4 you have to assume that the variances (within, between) of T and R are at least similar. » And you are using the geometric CV as the estimate of CVp for R?

Yes.

Dif-tor heh smusma 🖖
Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes Ing. Helmut Schütz 