ArticleBiometrics2023
Instrumental variable estimation of the causal hazard ratio.
Article in Biometrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Instrumental variable estimation of a hazard ratio for treatment with a waiting time without specifying its dependence on unmeasured confounders: application to a procedural registry.Lifetime data analysis · 2026Article
- Nonparametric Bayesian Adjustment of Unmeasured Confounders in Cox Proportional Hazards Models.Statistics in medicine · 2026Article
- Effectiveness of slow-release oral morphine versus other OAT regimens in key sub-populations: protocol for population-based target trial emulation.medRxiv : the preprint server for health sciences · 2025Article
- Article
- Multiplicative versus additive modelling of causal effects using instrumental variables for survival outcomes - a comparison.Statistical methods in medical research · 2025Article
- Propensity score matching for estimating a marginal hazard ratio.Statistics in medicine · 2024Article
- Article
- Instrumental variable estimation of the marginal structural Cox model for time-varying treatments.Biometrika · 2023Article
- Evaluation of instrumental variable method using Cox proportional hazard model in epidemiological studies.MethodsX · 2023Article
- Learning the Treatment Impact on Time-to-Event Outcomes: The Transcarotid Artery Revascularization Simulated Cohort.International journal of environmental research and public health · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cox's proportional hazards model is one of the most popular statistical models to evaluate associations of exposure with a censored failure time outcome. When confounding factors are not fully observed, the exposure hazard ratio estimated using a Cox model is subject to unmeasured confounding bias. To address this, we propose a novel approach for the identification and estimation of the causal hazard ratio in the presence of unmeasured confounding factors. Our approach is based on a binary instrumental variable, and an additional no-interaction assumption in a first-stage regression of the treatment on the IV and unmeasured confounders. We propose, to the best of our knowledge, the first consistent estimator of the (population) causal hazard ratio within an instrumental variable framework. A version of our estimator admits a closed-form representation. We derive the asymptotic distribution of our estimator and provide a consistent estimator for its asymptotic variance. Our approach is illustrated via simulation studies and a data application.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.