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@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@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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>>
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>>
>>
>>
>
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>


-- 
______________________

M.Sc. Klemens Weigl
Applied Statistics, Biostatistics
+43 680 215 67 58
klemens.weigl@gmail.com
www.psychologische-statistik.at
______________________

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