Analysis of regression discontinuity designs using censored data

TL;DR

This paper extends regression discontinuity design methods to censored data in medical research, proposing estimators that handle censoring bias and demonstrating their effectiveness through simulations.

Contribution

It introduces a novel estimation approach for RD designs with censored data using unbiased transformations, filling a gap in existing methodologies.

Findings

Proposed estimators effectively handle censored data in RD settings.

Simulation results show improved bias and variance properties.

Method applicable to medical studies with censored survival outcomes.

Abstract

In medical settings, treatment assignment may be determined by a clinically important covariate that predicts patients' risk of event. There is a class of methods from the social science literature known as regression discontinuity (RD) designs that can be used to estimate the treatment effect in this situation. Under certain assumptions, such an estimand enjoys a causal interpretation. However, few authors have discussed the use of RD for censored data. In this paper, 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 demonstrate the utility of the proposed methodology.