Nonparametric Regression Discontinuity Designs with Survival Outcomes

TL;DR

This paper introduces a nonparametric method for causal inference in survival analysis using regression discontinuity designs, addressing censoring issues with a doubly robust approach and providing an R package.

Contribution

It develops a novel nonparametric estimator for RDD with survival outcomes that handles censoring and covariate-dependent effects, with demonstrated advantages.

Findings

Enhanced efficiency and robustness in survival RDD estimations.

Applicable to multiple survival endpoints and long follow-up times.

Open-source R package 'rdsurvival' available for implementation.

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.