I have survey data from 1000 patients. This is a convenience venue-based sample. In a specific city, at 9 hospitals that happen to have a psychosocial program, patients can opt into the program if they want and complete this survey.
I am conducting a logistic regression on this data modeling whether patients report financial difficulty (binary outcome). Independent variables include a mix of demographic and clinical variables.
I would like to account for clustering within each site, since survey data at each hospital will likely share characteristics (because sites vary in treatments offered, demographics of surrounding geographic catchment areas, staff data collection practices, etc.). The "effect" of the site is not of theoretical interest - it is mostly a nuisance that I want to account for.
- With only 9 clusters, the vce(cluster site) option in Stata adjusting SEs for clustering is inappropriate. Regular SEs were recommended over cluster-adjusted SEs.
- I've also read that multilevel modeling with <10 clusters will likely be underpowered and is not recommended (though I'm not sure if this extends to logistic regression or only applies to multilevel models with continuous outcomes).
- I've seen other options proposed, but all in the context of OLS/linear models.
Would it be sufficient to just include hospital site as a control variable in the model? What is the best option to do this in my case of LOGISTIC regression and FEW clusters? (Or the least bad option?)