Dear all,
I'm uncertain about how to model the following in SPM.
I have data from a PET study involving 17 subjects, 2 within-subject conditions, and a covariate of interest that was measured for every subject and each condition. I'm not interested in the condition effect or condition*covariate interactions for this analysis. I just want to know what the main effect is of the covariate across subjects, and would ideally like to model this covariate by including the data from both conditions to improve the power in the model (e.g. using a random intercept model). If I were to simply average the PET data across conditions and perform a regression with the covariate of interest also averaged across conditions, I will lose power by halving the number of data points. I also cannot perform a regression including all 34 data points (17 subjects times 2 conditions) because the 2 observations per subject (i.e. 2 conditions) are not independent, and this will lead to lower P values than is realistic.
My understanding of the above problem is that a mixed model might be appropriate. So I'm wondering if the following would work. If I implement a flexible factorial design, I can include factors for 'subject' and 'condition', add the covariate of interest (without interacting with condition), and run a model in which the main effect is 'condition'. When selecting the contrast of interest, if I select '1' for the covariate, does this model the regression I am looking for and also control for the clustering of the observations (i.e. the 2 conditions) effectively? Or do I need to totally different model?
Many thanks
Chris
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