[R-sig-ME] partially crossed design, longitudinal

[Thierry Onkelinx]

Dear Christiano,

IMHO, the easiest solution would be to fit the model with the 5 level time
variable and then calculate the relevant post-hoc contrasts. e.g pert =
(pert early + pert late) / 2

Thinking about the analysis at the design stage of an experiment is
valuable.

Best regards,


ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

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2018-05-25 22:49 GMT+02:00 Cristiano Alessandro <cri.alessandro using gmail.com>:

> Hi all,
>
> I have a longitudinal study in which I measure the outcome variables at
> baseline condition (bas), then I apply a perturbation (pert) and I measure
> the outcome variable twice (early and late after perturbation is applied),
> and then I remove the perturbation (noPert) and I measure twice (early and
> late after perturbation is applied).
>
> I would like to use mixed models for this design, but I am a bit confused
> on how to do it. I could just have a single fixed effects 'time' with
> levels 1 to 5, where level 1 would be baseline, level 2 would be pert/early
> and so on. I think this is not the best design though. Alternatively, I
> could use a fixed effects 'condition' with levels bas, pert, noPert,
> crossed with another fixed effect 'time' with levels early/late. However,
> this last design has the problem that I do not have early/late for baseline
> actually.
>
> Do you have suggestion of what to do in a case like this?
>
> Thanks a lot
> Cristiano
>
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