Nonparametric Regression Discontinuity Designs with Survival Outcomes}

Thomas M. Menino Convention & Exhibition Center 

Quasi-experimental evaluations are critical for generating real-world causal evidence and complementing insights from randomized trials. The regression discontinuity design (RDD) is a quasi-experimental framework for estimating causal effects when treatment assignment depends on a running variable crossing a threshold. Such threshold-based rules are ubiquitous in healthcare, where predictive and prognostic biomarkers frequently guide treatment decisions. However, standard RDD estimators rely on complete outcome data, an assumption often violated in time-to-event analyses where censoring arises from loss to follow-up. To address this issue, we propose a nonparametric approach that leverages doubly robust censoring corrections and can be paired with existing RDD estimators. Our approach can handle multiple survival endpoints, long follow-up times, and covariate-dependent variation in survival and censoring. We discuss the relevance of our approach across multiple biomedical applications and demonstrate its usefulness through simulations and the PLCO prostate Cancer Screening Trial. We have also developed an open-source software package rdsurvival for the R language.

Causal inference

Regression discontinuity designs

Survival analysis

Statistical learning



Machine learning 

Biopharmaceutical Section