Please help me to interpret the findings of my model. The specifications of the model are:
Dependent variable: treatment (1) or no-treatment (0).
Independent variables: age, number of drugs used, comorbidity, others...
Multilevel structure: patients clustered within hospitals. Treatment rate varies across different hospitals. Multilevel logistic regression was used.
Findings: First, I ran the empty model with random intercept only and estimated the variance component (between hospital variance in treatment rate). Second, I added independent variables to the model one by one. Adding these variables either decreased or increased the variance component when comparing to empty model. Third, I added all the independent variables together into the model and variance component increased when comparing to empty model.
Conclusion: If I add variable to the model and variance decreases - this variable explains part of between-hospital variance in treatment rate. If I add variable to the model and it does not change variance component - this variable does not explain between-hospital variance.
Question: Please, give me an advice how can I interpret the fact that adding some variables to the model increase variance component.
Thank you in advance for any suggestions!