[R-sig-ME] Cumulative link mixed model appropriate in a 2x4 design?

Jarrod Hadfield j.hadfield at ed.ac.uk
Thu Sep 13 09:20:17 CEST 2012


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.



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
> 	[[alternative HTML version deleted]]
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