I am conducting a treatment evaluation. I am using an interrupted time series design (generalised linear mixed model; 42 monthly measurements per patient [18 pre-treatment, 24 post-treatment). I have assessed the impact of the treatment program on the level and trend of an adverse outcome for the whole sample (treatment vs no treatment) and would now like to assess whether the impact of treatment on the outcome is moderated by a statistically / clinically significant change in the provision of services. That is, is the effect of treatment "conditioned" on an increase in contact with health services.
Initially, I considered calculating change in contact using the reliable change index (e.g., baseline, mean amount of contact in X months before treatment; post-treatment, mean amount of contact in X months following treatment); however, I would really like to calculate change from the baseline level over time to the post-treatment level over time.
I have also considered comparing the baseline and post-treatment levels using another interrupted time series model, but my understanding is that the estimate for the post-treatment level will be for the time immediately following implementation of the treatment program and I am unclear how to extrapolate this to a level over time.
What is an appropriate way to calculate statistically / clinically significant change in the level of a variable using multiple observations?