Hi there, I'm conducting a difference in difference analysis for the first time. My aim is to compare the proportion of preterm deliveries (in a dataset of deliveries, 1 line per delivery) before and after a policy change. The control group are deliveries during the same time period in a year during which no policy change has happened. I do not expect any confounders so propensity scores or adjusting are not planned. I want to conduct a linear probability model to quantify the difference of the difference of the probability of preterm delivery between the two years with a robust 95% CI (because one mum could potentially contribute several deliveries to the dataset). My dataset is structured as follows: Exposed=1 if delivery in year of policy change, 0 if delivery in year without policy change Post=1 if delivery after date of policy change, 0 if delivery before date of policy change MumID Preterm Exposed Post 1 0 1 0 2 1 1 0 3 1 0 1 4 0 0 1 5 0 1 1 I've found the following code which seems to run. However, given I have not done this analysis before I'm unsure if I've implemented it correctly. proc surveyreg data=dataset;
cluster mumid; *I assume this calculates robust 95% CI by accounting for same Mumid;
class post exposed;
model preterm= post exposed post*exposed / CLPARM solution vadjust=none;
estimate "Diff in Diff" post*exposed 1 -1 -1 1;
lsmeans post*exposed;
run; Does 'cluster mumid' indicate to calculate robust 95% CI? I got the following preliminary output (sorry in German) and I'm wondering if I'm interpreting this right: I interpret this such that: the unexposed group (year without intervention) had 6.5% preterms prior to the policy change. The exposed group (year of policy change) had 0.1% more preterms prior to the policy change. I'm not sure how to interpret post 0 = -0.0063864. Is this the average change between pre and post policy change? I interpreted the interaction term as my main result: i.e. that the difference of the difference in the proportion of preterm deliveries between the two years is 0.4/100 deliveries, which is not statistically significant (p=0.203). So the policy change did not significantly change the proportion of preterm deliveries. Any insight into whether I'm reading this correctly or how to improve my code is appreciated, as I have not done this before. Many thanks, Julia
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