Comments (2)
Thanks for the additional comments @TomKellyGenetics. I have incorporated them into #73.
I merged the statement of need and summary and remodelled it slightly to improve the flow. I fixed the other comments as you indicated with the exception of
line 61: We often observe in our consulting activity that researchers try to implement a
hypothesis-based approach
as I don't see anything wrong there (MS Word also sees no issue with it...).
I highlight the pwr
and TrialSize
packages and also the Clinical Trials task view which lists a large number of packages that might be of relevance (although none cover precision based methods to our knowledge). I also cite R - thanks for pointing that out!
Furthermore, I also add you, @amoeba and @majensen to the acknowlegdements.
Regarding your question about the probability of an estimate being within the CI... there is a paper describing the method in the appendix if I remember but we need to think hard about how to do it for each of the different methods. It is something that we are considering adding in the future though, when time/resources allow. Stata actually has the option for the few methods available there, although their code is proprietory of course and not (easily) accessible, if at all.
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I've checked the updated paper and everything seems to be taken care of. It's a great concise summary of the package.
As a stylistic suggestion, I think this section would fit better in the Introductory section (following paragraph). It's a bit detailed for the "Summary".
There are many software packages for hypothesis-based sample size calculation, such as Stata, PASS,
G*Power, including many R packages, such aspwr
(@pwr) andTrialSize
(@trialsize; see other packages detailed on the CRAN Clinical Trials taskview). To the best of our knowledge, only Stata provides precision-based approaches, and only then for a small number of statistics.
I accept the manuscript as is and leave this change to the author's discretion.
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