Log in
RegisterOverpowering [Regulatives / Guidelines]
Hi all,
And furthermore things are complicated by the fact that we need Cmax and AUCt (and sometimes other metrics) to show BE, but when people dimension their studies they usually power things as if just one metric needs to be BE.
Example: We expect T/R 95% (yes, geometric mean on the observed scale
) and CV=24% (observed scale ) for AUCt and we expect T/R95% and CV 28% for Cmax.
Since Cmax is the worst case, we think we achieve 80% power with 34 evaluable subjects assuming the usual statistical stuff and with the implicit assumption that if Cmax is BE then AUCt will be as well. That works reasonably well in many cases, especially SODFs, because AUCt and Cmax tend to be positively correlated in the sense that if Cmax goes up then, all other factors being equal (which in itself can be a stretch), AUCt goes up as well.
Unfortunately the strength and mode of that correlation is in as good as all cases unknown. And for some formulations like DPIs it is outright nasty on the odd occasion.
In the absence of a good (as in valid and correct) model we therefore don't know the true power of any study even if the ordinary statistical assumptions hold. I'd like this topic to be better covered by stats literature...
To relate this boring story to the poster's questions I think we often take it for granted that if the worst-case is BE then the other will be as well. And in cases where we for some reason or other feel insecure about it, we could go for a higher (apparent; for one metric) power but we'd have no way of quantifying what 'higher' really should be or how to make 'higher' correspond to 'appropriate'.
This is real life. There should be more statisticians among CSO's in generic companies.
❝ My emphasis. The crucial point here is the term “required”. Some guidelines suggest 80–90% power (US-FDA, Canada-HPFB/TGD, Japan-NIHS, Brazil-ANVISA,…), whereas EMA uses the ambiguous phrase “appropriate”.
And furthermore things are complicated by the fact that we need Cmax and AUCt (and sometimes other metrics) to show BE, but when people dimension their studies they usually power things as if just one metric needs to be BE.
Example: We expect T/R 95% (yes, geometric mean on the observed scale
) and CV=24% (observed scale ) for AUCt and we expect T/R95% and CV 28% for Cmax.Since Cmax is the worst case, we think we achieve 80% power with 34 evaluable subjects assuming the usual statistical stuff and with the implicit assumption that if Cmax is BE then AUCt will be as well. That works reasonably well in many cases, especially SODFs, because AUCt and Cmax tend to be positively correlated in the sense that if Cmax goes up then, all other factors being equal (which in itself can be a stretch), AUCt goes up as well.
Unfortunately the strength and mode of that correlation is in as good as all cases unknown. And for some formulations like DPIs it is outright nasty on the odd occasion.
In the absence of a good (as in valid and correct) model we therefore don't know the true power of any study even if the ordinary statistical assumptions hold. I'd like this topic to be better covered by stats literature...
To relate this boring story to the poster's questions I think we often take it for granted that if the worst-case is BE then the other will be as well. And in cases where we for some reason or other feel insecure about it, we could go for a higher (apparent; for one metric) power but we'd have no way of quantifying what 'higher' really should be or how to make 'higher' correspond to 'appropriate'.
This is real life. There should be more statisticians among CSO's in generic companies.
—
Pass or fail!
ElMaestro
Pass or fail!
ElMaestro
Complete thread:
- Sample size vasup 2013-07-29 08:37
![[Mix]](https://static.bebac.at/img/mix.png "open in Mix view")
- Sample size ElMaestro 2013-07-29 10:00
- Sample size jag009 2013-07-29 15:57
- Overpowering Helmut 2013-07-30 15:51
- OverpoweringElMaestro 2013-07-30 16:37
- Overpowering jag009 2013-07-30 16:45
- Overpowering ElMaestro 2013-07-30 16:51
- Overpowering jag009 2013-08-01 20:08
- Overpowering kumarnaidu 2013-09-02 12:22
- Conservative study planning Helmut 2013-09-02 12:42
- Conservative study planning kumarnaidu 2013-09-02 14:36
- Conservative study planning Ohlbe 2013-09-02 18:44
- confidence limit of the CV Jay 2013-09-06 13:55
- CL of CV Helmut 2013-09-06 15:02
- CL of CV Jay 2013-09-06 15:21
- CL of CV Helmut 2013-09-06 15:42
- PE consideration Jay 2013-09-11 11:02
- PE consideration Dr_Dan 2013-09-11 12:07
- PE consideration Jay 2013-09-11 13:09
- PE consideration kumarnaidu 2013-09-11 13:22
- Sockpuppet? Helmut 2013-09-11 14:13
- PE consideration kumarnaidu 2013-09-12 13:04
- PE consideration Dr_Dan 2013-09-11 12:07
- PE consideration Jay 2013-09-11 11:02
- CL of CV Helmut 2013-09-06 15:42
- CL of CV Jay 2013-09-06 15:21
- CL of CV Helmut 2013-09-06 15:02
- Conservative study planning kumarnaidu 2013-09-02 14:36
- Conservative study planning Helmut 2013-09-02 12:42
- Overpowering kumarnaidu 2013-09-02 12:22
- Overpowering jag009 2013-08-01 20:08
- Overpowering ElMaestro 2013-07-30 16:51
- Overpowering jag009 2013-07-30 16:45
- OverpoweringElMaestro 2013-07-30 16:37
Ing. Helmut Schütz