Variance components – Proc mixed [Software]
❝ This is mindblowing. I can't say that I understand in any way, but it is clear that the unbounded (=unfiddled, native and pure) optimisation analysis results in the same residual as the all-fixed lm/anova.
I followed Yung-jin’s suggestions;
![[image]](img/uploaded/image219.png)
❝ An agonising choice indeed.
Yep.
❝ Why care about between-Vars in a 2,2,2-BE?
The only possible reason I can imagine: The cross-over turned out to be not such a good idea and you want to plan the next study in a parallel design. Then you would need the total (pooled) CV for sample size estimation. The common formula
\(CV_p\% = 100\sqrt{e^{(MS_s+MS_e)/2}-1}\)
would “work”, but result in values smaller than CVintra. Forget it. MSe MSs CVintra CVinter CVpooled
Cmax 0.0178034 0.0236397 13.40% 5.41% 14.47%
AUCt 0.0332878 0.0211136 18.40% NA 16.61%
AUCinf 0.0311744 0.0188434 17.79% NA 15.91%
❝ Why not just do the anova, fetch the residual, calculate a CI on basis of it…
Sure.
❝ …and punch any guy who asks about betweens hard in the face?
That’s Zan’s business.
❝ I wonder how the PK-workgroup at EMA would deal with this.
Not a problem for them, I guess. Doesn’t appear in their mandatory all fixed effects model (PHX) or
Proc GLM
. For the rest of the world (if running Proc MIXED
) I would go with the UNBOUND
option (see Detlew’s post). But – hey! – that’s not the code given in FDA’s guidances. 
Reading a lot of stuff it’s evident that the restriction to ≥0 results in biased estimates. With Yung-jin’s data set the resulting CI turned out to be liberal. Keeping patient’s risk in mind that’s not a good idea. Is the unrestricted method (accepting negative variances) always conservative? I think so, but I’m lacking the intellectual horsepower to prove it.
Dif-tor heh smusma 🖖🏼 Довге життя Україна!
![[image]](https://static.bebac.at/pics/Blue_and_yellow_ribbon_UA.png)
Helmut Schütz
![[image]](https://static.bebac.at/img/CC by.png)
The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes
Complete thread:
- How to calculate intersubject variability in PHX WinNonlin zan 2014-01-29 23:58 [Software]
- Negative variance component Helmut 2014-01-30 01:16
- Negative variance component zan 2014-01-30 18:11
- Negative variance component zan 2014-01-31 00:16
- Negative variance component ElMaestro 2014-01-31 08:20
- Negative variance component yjlee168 2014-01-31 10:26
- Example data set Helmut 2014-02-01 16:03
- Example data set yjlee168 2014-02-01 17:40
- PHX build 6.3.0.395 / 6.4.0.511 Helmut 2014-02-02 02:04
- PHX build 6.3.0.395 / 6.4.0.511 yjlee168 2014-02-02 07:54
- PHX build 6.3.0.395 / 6.4.0.511 Helmut 2014-02-02 02:04
- Example data set yjlee168 2014-02-01 17:40
- Example data set Helmut 2014-02-01 16:03
- Negative variance component ElMaestro 2014-02-01 16:31
- Negative variance component yjlee168 2014-02-01 17:47
- Just thinking loud ElMaestro 2014-02-01 19:02
- All models are wrong… Helmut 2014-02-02 02:31
- another book for linear model yjlee168 2014-02-02 08:07
- All models are wrong… Helmut 2014-02-02 02:31
- Just thinking loud ElMaestro 2014-02-01 19:02
- References Helmut 2014-02-02 02:19
- References ElMaestro 2014-02-02 09:56
- Negative variance component yjlee168 2014-02-01 17:47
- Negative variance component – Chow/Liu d_labes 2014-02-03 09:02
- Negative variance component – Chow/Liu ElMaestro 2014-02-03 10:22
- Variance components – Proc mixed d_labes 2014-02-03 11:58
- Variance components – Proc mixed ElMaestro 2014-02-03 12:58
- Variance components – Proc mixed 90% CIs d_labes 2014-02-03 13:16
- Variance components – Proc mixedHelmut 2014-02-03 14:14
- FDA code for non-replicate crossover? d_labes 2014-02-03 15:54
- Proc GLM rulez Helmut 2014-02-03 16:16
- Variance components – Proc mixed yjlee168 2014-02-03 20:43
- NOBOUND Helmut 2014-02-03 22:08
- FDA code for non-replicate crossover? d_labes 2014-02-03 15:54
- Variance components – Proc mixed ElMaestro 2014-02-03 12:58
- Variance components – Proc mixed d_labes 2014-02-03 11:58
- lm() or lme() for 2x2x2 study design? yjlee168 2014-02-03 20:22
- lm() or lme() for 2x2x2 study design? ElMaestro 2014-02-03 22:11
- lm() or lme() for 2x2x2 study design? yjlee168 2014-02-04 13:09
- bear for 2x2x2 study with negative variance components yjlee168 2014-02-05 19:12
- lm() or lme() for 2x2x2 study design? yjlee168 2014-02-04 13:09
- lm() or lme() for 2x2x2 study design? ElMaestro 2014-02-03 22:11
- Negative variance component – Chow/Liu ElMaestro 2014-02-03 10:22
- Negative variance component Helmut 2014-01-30 01:16