Presentation on theme: "Regression Discontinuity Design William Shadish University of California, Merced."— Presentation transcript:
Regression Discontinuity Design William Shadish University of California, Merced
Regression Discontinuity Design Units are assigned to conditions based on a cutoff score on a measured covariate, For example, communities that exceed a certain cutoff on arrests for drunk driving for young drivers per 100,000 receive treatment, and communities below that cutoff are in the comparison condition. The effect is measured as the discontinuity between treatment and control regression lines at the cutoff (it is not the group mean difference).
Advantages When properly implemented and analyzed, RD yields an unbiased estimate of treatment effect (see Rubin, 1977). Communities are assigned to treatment based on their need for treatment, consistent with how many policies are implemented.
Disadvantages Statistical power is considerably less than a randomized experiment of the same size. Careful attention to power is crucial. Effects are unbiased only if the functional form of the relationship between the assignment variable and the outcome variable is correctly modeled, including: –Nonlinear Relationships –Interactions
Citations to Med/PH Examples Cullen, K.W., Koehly, L.M., Anderson, C., Baranowski, T., Prokhorov, A., Basen-Engquist, K., Wetter, D., & Hergenroeder, A. (1999). Gender differences in chronic disease risk behaviors through the transition out of high school. American Journal of Preventive Medicine, 17, 1-7. Finkelstein, M.O., Levin, B., & Robbins, H. (1996a). Clinical and prophylactic trials with assured new treatment for those at greater risk: I. A design proposal. American Journal of Public Health, 86, Finkelstein, M.O., Levin, B., & Robbins, H. (1996b). Clinical and prophylactic trials with assured new treatment for those at greater risk: II. Examples. American Journal of Public Health, 86,
Improvements to the Design Modeling of functional form is improved if it can be observed prior to implementation of treatment (e.g., if archival data is used). Using all the standard methods to improve power (e.g., add covariates). Combining randomized and nonrandomized designs
Using Regression Discontinuity as a Design Element For those who are cut out of the experiment based on quantitative eligibility, continue to measure their outcome, and they can be added to the design to increase power. For those falling below a cutoff on a measure of outcome, or of receipt of treatment, give a booster and reanalyze that part of the data as an RDD.
Summary Of the designs being considered for this intervention, RD is the only one that yields an unbiased estimate. RD can be used with both archival data and original data. But there is question about whether it can be implemented with sufficient power in this case.