Regression-based proximal causal inference for right-censored time-to-event data

TL;DR

This paper introduces a novel two-stage regression proximal causal inference method tailored for right-censored survival data, addressing unmeasured confounding in observational studies with time-to-event outcomes.

Contribution

It develops a new PCI regression approach for right-censored data under an additive hazard model, with theoretical support and practical implementation in R.

Findings

Effective adjustment for unmeasured confounding in survival analysis

Application to SUPPORT study data demonstrates practical utility

Open-source R package 'pci2s' facilitates adoption

Abstract

Unmeasured confounding is one of the major concerns in causal inference from observational data. Proximal causal inference (PCI) is an emerging methodological framework to detect and potentially account for confounding bias by carefully leveraging a pair of negative control exposure (NCE) and outcome (NCO) variables, also known as treatment and outcome confounding proxies. Although regression-based PCI is well developed for binary and continuous outcomes, analogous PCI regression methods for right-censored time-to-event outcomes are currently lacking. In this paper, we propose a novel two-stage regression PCI approach for right-censored survival data under an additive hazard structural model. We provide theoretical justification for the proposed approach tailored to different types of NCOs, including continuous, count, and right-censored time-to-event variables. We illustrate the approach with an evaluation of the effectiveness of right heart catheterization among critically ill patients using data from the SUPPORT study. Our method is implemented in the open-access R package 'pci2s'.