Dear list members,

I am looking for help to find a proper model for some data I collected, I am not sure that this message is appropriate for this list, I apologize if this is not the case.

I measure a longitudinal binary outcome (correctness of detection, 0: incorrect, 1: correct) with respect to 5 different experimental conditions (1 baseline and 4 treatments). The outcome is always measured at the same 10 time points. Each of the 9 subjects participated in all 5 conditions. Additionally, for each subject and condition, the experiment was replicated 36 times. I therefore end up with 9*5*36=1620 binary longitudinal series (= trials of 10 points each).

My aim is to assess the influence of the experimental condition on my binary outcome. I need to build a model that would take into consideration the correlation along time for a given trial and the correlation among trials for a given subject.

I am considering a 3 levels logistic models where 10 consecutive binary measurements (level 1) are obtained on replicates (level 2) which are clustered into subjects (level 3). My only level 1 covariate would be the time of measurement (ordinal factor, T = 1, ..., 10) and as level 2 covariate, I consider the experimental condition. I don't consider any level 3 covariate per se, but still want the model to account for between-subject variability.

As there are many ways to look at those data, I'd be grateful to receive some input to know whether I'm on the right track and some insight as to which R package is most suited for the analysis I want to perform.

Thanks!
Adrien



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