I have been asked by a colleague for help to perform a sample size
calculation. As this is not an area I'm familiar with, I was hoping
someone in this group might give me feedback to check I'm doing this
correctly.
My colleague plans to sample 2n people, randomized to treated and
untreated groups (each of size n). A single baseline measurement of
four indicators will be taken from all individuals, and he has provided
expected means and standard deviations for these. The treated group
will then be treated, and the measures repeated (again, he has provided
expected means and SDs for these).
He wants to be powered to show there is a change in the treated group,
but not the untreated.
He is not sure how best to analyse these data, but looking at the
documentation for sampsi, I note there are 3 common methods listed -
post, change, and ancova. I assume all would be appropriate, though the
latter two more powerful.
So I need to do
sampsi mean_post_treatment mean_pre_treatment, sd1(sd_post_treatment)
sd2(sd_pre_treatment) pre(1) post(1) meth(all)
r01(corr_pre_post_treatment)
But then I also need to adjust for the multiple testing of four
measures. As a conservative approach, I was planning to just append
alpha(0.0125)
to force a Bonferroni-type correction and maintain a overall 0.05
significance level.
I would appreciate any advice, particularly if I'm making some big
error!
What Chris's colleague really seems to need is expected within-group
standard deviations of the post-treatment - pre-treatment difference. That
way, he could carry out the power calculations so as to compare two groups
of differences, and calculate confidence intervals and P-values for the
difference between group mean differences. The variance of a post-treatment
- pre-treatment difference is equal to
