I agree with the others that you should consult with a statistician, but here are some additional things to consider:
The usual equivalence test can be done much simpler (both computation and conception) by just calculating a confidence interval on the difference and seeing if the entire interval is within the equivalence region. But you may also want to look at non-inferiority. But the straight forward equivalence testing may not be the best option, consider this comparison: Lets assume that systolic blood pressure in our population of interest has a mean of 120 with a standard deviation of 20 (those are actually fairly close to truth for healthy adults), my new test is to flip a fair coin and report a systolic blood pressure of 20 if the coin is tails and a systolic blood pressure of 220 if the coin lands heads. These 2 distributions have the same mean of 120, so with a large enough sample size we can show these 2 to be equivalent (according to the mean). But I for one do not want my doctor using the second approach in deciding if I need medication for high or low blood pressure. What if I have 2 measures that have a perfect monotonic relationship, but different means for my population. This means that they are not equivalent, but knowing one value would give me a perfect knowledge of the other (just need some calculations/adjustment), I would definitely prefer this to the coin flipping above. I would suggest googling for "Bland Altman", they are 2 authors of several papers, tutorials, websites, etc. that talk about many of these issues is more depth looking at better approaches in many cases than a simple equivalence test. On Thu, Mar 27, 2014 at 7:53 AM, Manuel Carona <unku...@gmail.com> wrote: > Hi, > > I have implemented a therapeutic intervention on two groups (one is a > control group) and tested them in two moments using some assessment > tools (with normative data). Now I want to compare the experimental > group with the control group using clinical equivalence testing. To do > this I need to specify a range of closeness (One for each assessment > tool according to the specificity of this same tool) and do two > one-tailed tests to test if the two groups are considered clinically > equivalent in the first moment and on the end I want to compare the > experimental group with the normative data (Here I have to add the mean > and standard deviation of the normative sample because I don't have the > normative sample). > > I know that R has a package named equivalence but I don't know how to do > this kind of calculations with it. Is it even possible with the actual > packages? > > Thanks in advance > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- Gregory (Greg) L. Snow Ph.D. 538...@gmail.com ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
