I am planing a pre-post treatment-control design study with a large number of pre-treatment measurements. I have subjects divided into a control group and a treatment group. For both groups, I will collect hourly data for one year prior to the start of the treatment and then continue collecting data for another year. This will yield approximately 9000 pre-treatment measurements and 9000 post treatment measurements for each subject.
The treatment is something that cannot be stopped once it is started, so a cross-over design could only be of the form AA/AB, which won't take advantage of the benefits of that type of design.
The psychological and bio-medical literature suggests using an ANCOVA model, where the pre-treatment data is used as a covariate in the model. Putting 9000 covariates in a model seems totally ridiculous. Also, reducing the pre-treatment data to a summary statistic doesn't take advantage of the large number of measurements.
I'm sure that this must have come up before, any ideas? References to published results would be especially helpful.