Bioequivalence and Bioavailability Forum

Dear Detlew!

❝ ❝ […] Unfortunately Dunnett is only applicable for nominal scales, not for continuous ones (doses).

❝ (emphasis by me)

❝ Where does this opinion come from . AFAIK is Dunnett's test a post-hoc test within the ANOVA framework to compare many means to one control. ANOVA always deals with measurements on continuous (metric) scales. Or do I miss somefink here?

No, no – sorry. Should have done my homework before. I had in mind that Dunnett is applicable if a continuous variable if tested based on an ordered (nominal) covariate. You are right.

❝ Moreover Hauschke, Steinijans and Pigeot […]. See page 170 of the reference. The argumentation given is plausible for me also as an amateur in statistics I'm not able to prove it.

Yes, sounds good for another amateur. Now for the big but (p170–171):

[…] no adjustment of the comparisonwise type I error is needed to keep the familywise type I error under control. However, this intersection-union testing procedure inflates the type II error, that is the probability of erroneously failing to reject at least one of the null-hypotheses. This inflation has to be taken into account by an adequate sample size determination.
(my emphasis)

Your reference lead my again to CPMP’s ’Points to Consider on Multiplicity Issues in Clinical Trials’ (2002). My gut-feeling seems to be right at the end: more comparisons = less power. ‘Adequate sample size determination’ smells like the respective section in the BE GL. Let’s look at the CPMP document:

This procedure inflates the relevant type II error […], which in the worst case scenario is the sum of the type II error errors connected with the individual hypotheses.

Oh wow!

❝ BTW: Where does the 2-stage design come into play for dose-proportionality studies?

For some background see the end of this post. I have the frivolous idea to plan for a similar study. If I want to go with the worst-case scenario (3 simultaneous comparisons, each at β 0.20/3) in my specific case (T/R 0.95, CV 20%) I would end up with a sample size of 28 instead of 20 in a single comparison with 80% power. +40% penalty.

BTW: Can you please check the new requirements for R-packages? Since the update to R 2.14.0 the help-files of PowerTOST are not accessible any more. Has something to do with a missing vignette / base URL?