  In medical settings, treatment assignment may be determined by a clinically
important covariate that predicts patients' risk of event. 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 assumptions, such an estimand enjoys a causal
interpretation. However, few authors have discussed the use of RD for censored
data. In this paper, 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 censored weighting and doubly robust transformation
schemes. Simulation studies demonstrate the utility of the proposed
methodology.
