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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