Hi,   I am unsure if I am using the best method to analyze my dataset and would like some help/feedback on this.   I am looking at 8 states from 2010 to 2014 at a monthly level. 5 out of the 8 states eventually receive the treatment, which is the implementation of a mandatory state policy. The time at which the policy was implemented varies from state to state. Also, in some of these 5 states, there is a prior non-mandatory phase, where the policy is suggested but not required. So there are a total of 3 phases total: no policy, non-mandatory phase, mandatory phase.    The outcome is child deaths per state-year-month (these deaths are related to the policy being considered).   The research question is as follows: 1) Is this policy effective in reducing child deaths?   How can this best be analyzed?     I wonder if the following model will do the trick:  Poisson regression model Y = B0 + B1 Time + B2 Group + B3 Time * Group;   where Y = number of child deaths offset by total number of children Time: 1 = no policy period | 2 = non-mandated period | 3 = mandated period, Group: 1 = Never issued policy | 2 = issued a mandatory policy | 3 = issued a mandatory policy + had a non-mandatory phase. And with a random intercept effect to cluster all states together.    Any feedback is greatly appreciated.   Thank you. ... View more