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Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Instrumental variable (IV) analysis is widely used for causal inference.
  • Existing IV methods for time-to-event data often rely on heuristic approaches or restrictive assumptions.
  • Rigorous methods have limitations, such as excluding the Cox model or requiring dichotomous exposure/instruments.

Purpose of the Study:

  • To develop and evaluate novel instrumental variable (IV) estimators for time-to-event outcomes.
  • To extend IV analysis to structural Cox models with arbitrary exposure and instrument variables.
  • To provide a more robust framework for causal inference in epidemiological studies.

Main Methods:

  • Proposed a novel class of instrumental variable (IV) estimators.
  • Derived the asymptotic properties of the proposed estimators.
  • Utilized structural Cox models to accommodate time-to-event endpoints.
  • Allowed for arbitrary (continuous or discrete) exposure and instrument variables.

Main Results:

  • Developed new IV estimators for time-to-event data under a structural Cox model.
  • Demonstrated the asymptotic properties of the proposed methods.
  • Illustrated the methodology with real-world epidemiological data and simulations.

Conclusions:

  • The proposed IV methodology offers a more flexible and rigorous approach to causal inference for time-to-event data.
  • This extends the applicability of IV analysis in epidemiological research.
  • The methods provide a valuable tool for estimating treatment effects when confounding is present.