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RegisterBear to bear interval with 90% confidence [🇷 for BE/BA]
Hi again,
Yes, this is actually the easy part.
If you use something like this:
TotallyFantasticFit=lm(lnAUC~Subj+Seq+Per+Trt)
Then you can get the difference in LSMeans via:
summary(TotallyFantasticFit)
Note that the sign of the treatment effect will be depending on the way the data have been garbled initially: It will be equal to either (LSMean(T)-LSMean(R)) or to (LSMean(R)-LSMean(T)) so be aware of the sign.
The alternative way to do it is to simply calculate LSMeans by averaging over the sequences, see Chow & Liu's book.
It is solely a matter of taste.
Your end result (T/R ratio) will be the same regardless. And it will work for balanced as well as unbalanced datasets.
EM.
Edit 02.04.09: Error in my explanation of LSMean calculation.
❝ ❝ And this is exactly what we need for the BE evaluation,
❝ ❝ because log(LSMean(T)/LSMean(R))=log(LSMean(T)-log(LSMean(R))[...]
❝
❝ Interesting points. Do you mean for unbalanced data or for all kinds
❝ here? I don't know this before.
Yes, this is actually the easy part.
If you use something like this:
TotallyFantasticFit=lm(lnAUC~Subj+Seq+Per+Trt)
Then you can get the difference in LSMeans via:
summary(TotallyFantasticFit)
Note that the sign of the treatment effect will be depending on the way the data have been garbled initially: It will be equal to either (LSMean(T)-LSMean(R)) or to (LSMean(R)-LSMean(T)) so be aware of the sign.
The alternative way to do it is to simply calculate LSMeans by averaging over the sequences, see Chow & Liu's book.
It is solely a matter of taste.
Your end result (T/R ratio) will be the same regardless. And it will work for balanced as well as unbalanced datasets.
EM.
Edit 02.04.09: Error in my explanation of LSMean calculation.
Complete thread:
- Bear to bear interval with 90% confidence d_labes 2009-04-01 17:00
![[Mix]](https://static.bebac.at/img/mix.png "open in Mix view")
- Bear to bear interval with 90% confidence ElMaestro 2009-04-01 19:01
- Bear to bear interval with 90% confidence yjlee168 2009-04-01 20:38
- Bear to bear interval with 90% confidenceElMaestro 2009-04-01 21:28
- Bear to bear interval with 90% confidence yjlee168 2009-04-01 20:38
- Bear to bear interval with 90% confidence yjlee168 2009-04-01 20:15
- Bear to bear interval with 90% confidence ElMaestro 2009-04-01 21:47
- Bear to bear interval with 90% confidence yjlee168 2009-04-04 23:11
- Bear to bear interval with 90% confidence ElMaestro 2009-04-05 13:23
- Bear to bear interval with 90% confidence yjlee168 2009-04-06 07:50
- Data set d_labes 2009-04-07 08:46
- Data set yjlee168 2009-04-07 13:44
- Bear to bear interval with 90% confidence ElMaestro 2009-04-05 13:23
- Bear to bear interval with 90% confidence yjlee168 2009-04-04 23:11
- Models (not necessarily nice looking young woman) d_labes 2009-04-06 10:49
- Models (not necessarily nice looking young woman) yjlee168 2009-04-07 14:05
- Bear to bear interval with 90% confidence ElMaestro 2009-04-01 21:47
- Bear to bear interval with 90% confidence ElMaestro 2009-04-01 19:01
Ing. Helmut Schütz