# 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!

• Why do you need to analyse the two groups separately ? Can you explain a little more about how the study was designed and how the data were collected ? Please edit the question with this info. – Robert Long Mar 23 at 21:06

In R syntax that would be (R automatically includes the intercept):
Post - Pre Score ~ Intercept + B1*Covariate 1 + B2*Covariate 2 + ...