Nonparametric Regression Discontinuity Designs with Survival Outcomes

2026-04-03

AI Summary

This study addresses the challenge of analyzing survival outcomes in regression discontinuity designs (RDD) when censoring renders standard methods invalid. The authors propose a novel nonparametric estimator that, for the first time, integrates a doubly robust censoring correction mechanism into the RDD framework. This approach effectively accommodates complex censoring patterns—including multiple endpoints, long follow-up periods, and covariate-dependent censoring—while maintaining robustness under model misspecification and improving estimation efficiency. The method is implemented in the accompanying R package rdsurvival. Extensive simulations and an empirical application to the PLCO prostate cancer screening trial demonstrate that the proposed estimator substantially outperforms existing conventional approaches.

Abstract

Quasi-experimental evaluations are central for generating real-world causal evidence and complementing insights from randomized trials. The regression discontinuity design (RDD) is a quasi-experimental design that can be used to estimate the causal effect of treatments that are assigned based 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 RD 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 RD 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 areas of applications and demonstrate its usefulness through simulations and the prostate component of the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial where our new approach offers several advantages, including higher efficiency and robustness to misspecification. We have also developed an open-source software package, $\texttt{rdsurvival}$, for the $\texttt{R}$ language.

Problem

• Regression Discontinuity Design
• Survival Outcomes
• Censoring
• Nonparametric Estimation
• Quasi-experimental Evaluation

Innovation

• nonparametric
• regression discontinuity design
• survival outcomes
• doubly robust
• censoring correction