A0205
Title: Analysis of regression discontinuity designs using censored data
Authors: Youngjoo Cho - Konkuk University (Korea, South) [presenting]
Chen Hu - Johns Hopkins University School of Medicine (United States)
Debashis Ghosh - University of Colorado Anschutz Medical Campus (United States)
Abstract: In medical studies, the treatment assignment may be determined by a clinically important covariate that predicts patients' survival risk. There is a class of methods from the social science literature known as regression discontinuity (RD) designs that can be used to estimate the treatment effect in this situation. Under certain conditions, such an effect enjoys a causal interpretation. However, few authors have discussed the use of RD for censored data. We show how to estimate causal effects under the regression discontinuity design for censored data. The proposed estimation procedure employs a class of censoring unbiased transformations that includes inverse probability weighting and a doubly robust transformation. Simulation studies demonstrate the utility of the proposed methodology.
