partially crossed design, longitudinal

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 at 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 at 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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