Geometric mean and CV [Power / Sample Size]

posted by Helmut Homepage – Vienna, Austria, 2019-09-06 20:15 (1690 d 22:43 ago) – Posting: # 20549
Views: 6,302

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 🖖🏼 Довге життя Україна! [image]
Helmut Schütz
[image]

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

Complete thread:

UA Flag
Activity
 Admin contact
22,993 posts in 4,828 threads, 1,649 registered users;
66 visitors (1 registered, 65 guests [including 6 identified bots]).
Forum time: 18:58 CEST (Europe/Vienna)

If you don’t like something change it;
if you can’t change it, change the way you think about it.    Mary Engelbreit

The Bioequivalence and Bioavailability Forum is hosted by
BEBAC Ing. Helmut Schütz
HTML5