I've got a study, a before-and-after controlled design.
I have pre-intervention data and post-intervention data, the intervention is an educational intervention, given to general practitioners and the outcomes (a continuous outcome and a binary outcome) are measured on patients. Therefore, the pre-intervention group is different to the post-intervention group. I also have a control group, collected over the same time period. So I have n1 pre-intervention control, n2 post-intervention control, n3 pre-intervention active and n4 post-intervention active patients.
I'd be grateful on your thoughts on how to evaluate the intervention. The groups (n1, n2, n3 and n4) are small, roughly 25-30. Thus I am also assuming there is no underlying trend in the change in the outcome prior or post intervention (I have insufficient numbers for an interrupted time series approach or segmented regression).
Clearly calculating the difference in post-intervention data ignores any potential differences in pre-intervention levels. Therefore, one approach could be to calculate 83.4% confidence intervals in the pre-post difference in both the control and active arms and observe if the two 83.4% CIs overlap to draw conclusions at the 95% level.
Any suggestions on alternative approaches?