Geometric mean and CV [Power / Sample Size]

posted by Helmut Homepage – Vienna, Austria, 2019-09-06 18:15 (875 d 21:53 ago) – Posting: # 20549
Views: 5,011

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?


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