Geometric mean and CV [Power / Sample Size]

posted by Helmut Homepage – Vienna, Austria, 2019-09-06 20:15 (2130 d 21:02 ago) – Posting: # 20549
Views: 9,157

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
23,428 posts in 4,929 threads, 1,680 registered users;
64 visitors (0 registered, 64 guests [including 25 identified bots]).
Forum time: 17:17 CEST (Europe/Vienna)

No matter what side of the argument you are on,
you always find people on your side
that you wish were on the other.    Thomas Berger

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