Cumulative link mixed model appropriate in a 2x4 design?

Dear R-sig-mixed-model-group!

Basically I've got a fairly simple dataset with a 2x4 design (two
independent variables = i.v.) and a continuous response variable (only one
dependent variable).
1st i.v.: two different treatments
2nd i.v.: 4 time points: after 2, 4, 6 and 8 weeks --> at each time
point: mice with tumor cells are killed and the tumor growth was analyzed.
Therefore no repeated measures. Every mouse can be just in one of the two
treatment groups in just one of the 4 time points.
The data are normally distributed, but with unequal and small 'n' in each
group (ranging from 8 to 14 mice per group).

Objective: to test wether or not one treatment is better than the other
treatment over the 4 time points all together?

Someone was suggesting "cumulative link mixed model with Laplace
approximation" for this task.

Well I am wondering if the clmm with Laplace approximation is appropriate
for this task, because the response variable is "continuous" and not
ordinal (as written in the clmm2_tutorial) Am I loosing much power if I
apply it?

I'd be interested if someone might have some arguments for or against the
application of clmm with L.a. in that design-setting - or a better solution?

Kind regards,
Klemens

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Dear David and Jarrod,

so far I tried a lot :-).

1st solution:
To run a linear mixed model with 'the 2 groups/treatments' as fixed effect
and 'the 4 time points' as random effect. It showed a significant group
effect. Then it is interesting on which time point. My colleagues just want
4 t-tests for independent measures at each time point. If run them it
showed - Bonferroni-corrected - only one significant time point.

1) Is this approach okay?


2nd solution:
I also wanted to apply the cumlutative link mixed model with L.a..
--> the response was 'size' (continuous)
--> fixed effect 'treatment' (2 groups)
--> random effect 'weeks' (4 time points).
The problem: I always got:
----------------------------------------
FEHLER:
response needs to be a factor
----------------------------------------

I used the following R-code:
------------------------------------------

library(ordinal)

...

fm1 <- clmm2(size ~ treatment, random=weeks, data=data)

and then:

fm1 <- clmm2(size ~ treatment, random=weeks, data=data, Hess=TRUE,

nAGQ=10)
------------------------------------------

In the wine data example of the clmm2-tutorial the bottles of wine first
were rated from 0 to 100 and then 'transformed' into the rating of 1=
"least bitter", 5= "most bitter".

2) Do I also have to transform the continuous 'size' response into
something like a 1 to 5 rating, before I can apply the clmm2-model?

First I thought there - compared to t-tests for independent samples where
one also can easily apply the Mann-Whitney-U-test if the  data are
non-normal... - might also be no problem just to apply clmm2 and it should
at least show me some results.
But it showed: response needs to be a factor.

3) Is the problem just 2) or lies the problem somewhere else?

I appreciate your answers.

Kind regards,
Klemens






2012/9/13 Jarrod Hadfield <j.hadfield at ed.ac.uk>

Hi,

With normal response, you are right in thinking that you don't need a
cumulative link mixed model. A linear mixed model (with group as a random
term?) should suffice.

Cheers,

Jarrod






Quoting Klemens Weigl <klemens.weigl at gmail.com> on Wed, 12 Sep 2012
15:58:44 +0200:

 Dear R-sig-mixed-model-group!

Basically I've got a fairly simple dataset with a 2x4 design (two
independent variables = i.v.) and a continuous response variable (only one
dependent variable).
1st i.v.: two different treatments
2nd i.v.: 4 time points: after 2, 4, 6 and 8 weeks --> at each time
point: mice with tumor cells are killed and the tumor growth was analyzed.
Therefore no repeated measures. Every mouse can be just in one of the two
treatment groups in just one of the 4 time points.
The data are normally distributed, but with unequal and small 'n' in each
group (ranging from 8 to 14 mice per group).

Objective: to test wether or not one treatment is better than the other
treatment over the 4 time points all together?

Someone was suggesting "cumulative link mixed model with Laplace
approximation" for this task.

Well I am wondering if the clmm with Laplace approximation is appropriate
for this task, because the response variable is "continuous" and not
ordinal (as written in the clmm2_tutorial) Am I loosing much power if I
apply it?

I'd be interested if someone might have some arguments for or against the
application of clmm with L.a. in that design-setting - or a better
solution?

Kind regards,
Klemens

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