Analysis of regression discontinuity designs using censored data


  • Youngjoo Cho Department of Applied Statistics, Konkuk University, Seoul, Republic of Korea
  • Chen Hu Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
  • Debashis Ghosh Department of Biostatistics and Informatics Colorado School of Public Health, Aurora, CO 80045, USA


Causal effect; Double robustness; Instrumental variable; Observational studies; Survival analysis.


In many medical and scientific settings, the choice of treatment or intervention may be determined by a covariate threshold. For example, elderly men may receive more thorough diagnosis if their prostate-specific antigen (PSA) level is high. In these cases, the causal treatment effect is often of great interest, especially when there is a lack of evidence from randomized clinical trials. From the social science literature, a class of methods known as regression discontinuity (RD) designs can be used to estimate the treatment effect in this situation. Under certain assumptions, such an estimand enjoys a causal interpretation. 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 are used to evaluate the finite-sample properties of the proposed estimator. We also illustrate the proposed method by evaluating the causal effect of PSA-dependent screening strategies.

Journal of Statistical Research 2021, Vol. 55, No. 1, pp. 225-248





How to Cite

Cho, Y., Hu, C., & Ghosh, D. . (2021). Analysis of regression discontinuity designs using censored data. Journal of Statistical Research, 55(1), 225–248. Retrieved from