Regression discontinuity design with right-censored survival data

TL;DR

This paper adapts regression discontinuity design to right-censored survival data using a local polynomial Aalen estimator within an intensity-based framework, providing large-sample theory and confidence intervals.

Contribution

It introduces a novel local polynomial Aalen estimator for RDD in survival analysis with censored data, extending causal inference methods to this setting.

Findings

Developed large-sample properties for the estimator

Constructed confidence intervals accounting for bias correction

Embedded models within the potential outcomes framework

Abstract

In this paper the regression discontinuity design is adapted to the survival analysis setting with right-censored data, studied in an intensity based counting process framework. In particular, a local polynomial regression version of the Aalen additive hazards estimator is introduced as an estimator of the difference between two covariate dependent cumulative hazard rate functions. Large-sample theory for this estimator is developed, including confidence intervals that take into account the uncertainty associated with bias correction. As is standard in the causality literature, the models and the theory are embedded in the potential outcomes framework. Two general results concerning potential outcomes and the multiplicative hazards model for survival data are presented.