can I use a multilevel model for my situation? Pre/post, no control group I have a question about whether multilevel modeling is appropriate in my situation. I’m  working on an analysis looking at the effect of a treatment for patients with a disease. There is one pre measurement and one post measurement, and no control group. I have several covariates (e.g., demographics). Outcome is continuous.
Research questions:
    • what is effect of the treatment?
    • How do covariates such as demographics influence the outcome?
I was thinking of a linear mixed model:
    • random intercept for subject
    • dummy variable: 1 for post period, 0 for pre period. interpreted as the treatment effect
    • covariates as themselves in model (i.e., not interacted with any other variables)
None of the covariates are time-varying.
Seems that an alternative approach would be to run a normal linear regression, with the outcome being the post period measurement, and the pre period measurement as an independent variable. Other variables would be included as they are. I haven't been able to easily find examples of doing multilevel modeling with pre/post data, so I’m wondering if a multilevel modeling approach really requires more measurements (e.g. 3 or more time points).
There were some subjects who withdrew from the study, but I can’t get their data. Sample size isn't great - 34 for one set of people, and 19 for another set. (I have 2 groups of people that need to be analyzed separately). Note: the two group are analyzed separately because one of them represents patients with the disease and in the second case, the patients don’t have the disease.
Any thoughts?
Thank you!
 A: If I understand the study correctly you have just two outcome measurements per patient: one before treatment and one after. If that's the case then you can simply compute a single score, Post - Pre, for each patient. Then you can run a normal regression model with that score as your dependent variable and the covariates you mentioned as your independent variables (regressors):
In R syntax that would be (R automatically includes the intercept):
Post - Pre Score ~ Intercept + B1*Covariate 1 + B2*Covariate 2 + ...
The beta values and t-statistics for each covariate will tell you how much each covariate was a good predictor of the efficacy of the treatment. The intercept of the model will be the effect of the treatment alone. That's because the intercept is a test of whether the mean of the dependent variable (Post - Pre Score)!= 0. Asking if the difference score != 0, is the same as asking if there was a difference between the mean Post score and the mean Pre score, accounting for the fact that each patient was measured twice (i.e. repeated measures design which is why I'm inferring you thought about multi-level modeling in your question).
