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Re: st: Sample size calculation
My previous experience with cytokines is that (if measured well) they
should have an approximately log-normal distribution. Therefore, to compare
2 groups of cytokine measurements, the correct approach is probably to
define a confidence interval for a ratio of geometric means. To do power
calculations for this, you need to know two main things, firstly the size
of a clinically interesting geometric mean ratio to be detected, and
secondly the likely within-group variability of the logs (which may be
measured by coefficient of variation, SD of the logs, interpercentile
ratio, or sometimes by geometric SD).
Power calculations for lognormally distributed variables are discussed in
Newson (2004), which can be downloaded from my website (see my signature
below) either using a browser or by typing within Stata
net describe powergen, from(http://www.kcl-phs.org.uk/rogernewson/papers)
and getting the ancillary file -powergen.pdf-. Geometric means and their
ratios in Stata are discussed in Newson (2003), which can also be
downloaded from my website either using a browser or typing within Stata
net describe gmratio, from(http://www.kcl-phs.org.uk/rogernewson/papers)
and getting the ancillary file -gmratio.pdf-. Another good source on the
lognormal distribution is Stanislav Kolenikov's website at
which features a very useful reference with formulas at
I hope this helps.
Newson R. 2003. Stata tip 1: The eform() option of regress. The Stata
Journal 3(4): 445. Also downloadable from my website at
Newson R. 2004. Generalized power calculations for generalized linear
models and more. The Stata Journal 4(4): 379-401. Also downloadable from my
At 16:01 26/10/2005, Ronan wrote:
On 26 DFómh 2005, at 14:19, <email@example.com>
We are planning to measure some cytokines in patients with MS
between a group with treatment and control, but there have not been
literatures regarding these measurements, so we do not have an
mean and standard deviation. Is there a way to estimate a sample size?
(our IRB really wants to see sample size estimate.) Thanks.
You really are in the dark, then.
There are several approaches. The Resource Equation method
http://embryo.ib.amwaw.edu.pl/invittox/er/ER/ER%2029.pdf see page 9
is useful in a preliminary experiment. It's based on the idea that
the amount of information in each new piece of data diminishes as the
sample size increases.
It allows you to estimate the sample size needed to ascertain whether
a study would be useful or not. It is often used in animal research
where investigators have no idea at all of what they might find.
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