regression discontinuity design

Illustrating Value, Prioritizing Evaluation, Saving Lives

I recently co-authored an article with Melissa LD Christopher that is now posted on the National Resource Center for Academic Detailing (NaRCAD). Although the goal was to highlight the importance of performing program evaluations, the article also reports some of our findings with the Veterans Health Administration National Academic Detailing Service's impact on naloxone distribution.

In a retrospective, repeated measures cohort study, we reported that providers who were exposed to academic detailing had a greater rate of naloxone distribution compared to providers who were unexposed to academic detailing. This difference-in-differences estimation was significant at the alpha level of 0.05. The remarkable feature of our report is that academic detailing had a significant association with naloxone distribution. However, due to selection bias, which was not taken into account in our preliminary analysis, these findings may be limited.

In order to address selection bias, I will use a regression discontinuity design, which can mitigate selection bias and yield a causal interpretation. An important element of regression discontinuity design is the selection of a running (treatment assignment) variable. If the running variable has a distinct discontinuity for treatment assignment at a certain cut-off, it is considered a "sharp" regression discontinuity. However, if the probability of treatment assignment is not distinct, then it is considered a "fuzzy" regression discontinuity.