ArticleEpidemiology (Cambridge, Mass.)2015
Instrumental variable estimation in a survival context.
Article in Epidemiology (Cambridge, Mass.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 1 of them a synthesis that pooled it.
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Who cites it
72 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Statistical methods for non-adherence in non-inferiority trials: useful and used? A systematic review.BMJ open · 2022Pooled it
- Early vs Deferred Non-Messenger RNA COVID-19 Vaccination Among Chinese Patients With a History of Inactive Uveitis: A Randomized Clinical Trial.JAMA network open · 2023Trial
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- Effect of exogenous testosterone on cardiovascular, cerebrovascular, and thromboembolic adverse events: results of three complementary research designs.American journal of epidemiology · 2026Article
- Permanent cognitive or physical impairment after transfer from long-term care to acute care: a retrospective cohort study.Age and ageing · 2026Article
- The causal impact of older siblings' academic achievement on younger siblings' risk for drug use disorder: instrumental variable and propensity score analyses.Psychological medicine · 2026Article
- Evidence triangulation in health research.European journal of epidemiology · 2025Article
- Total healthcare costs of deinstitutionalized long-term care provision in the Netherlands: an instrumental variable analysis.BMC health services research · 2025Article
- Inotropes and mortality in patients with cardiogenic shock: an instrumental variable analysis from the SWEDEHEART registry.European heart journal. Cardiovascular pharmacotherapy · 2025Observational
- The Role of Dimensions of Social Support in the Relationship Between Stigma and Mental Health: A Moderation Analysis.AIDS and behavior · 2025Article
- Longitudinal association between neighborhood-level social capital and incidence of major psychiatric disorders in a cohort of 1.4 million people in Sweden.Nature. Mental health · 2025Article
- Multiplicative versus additive modelling of causal effects using instrumental variables for survival outcomes - a comparison.Statistical methods in medical research · 2025Article
- Reading and conducting instrumental variable studies: guide, glossary, and checklist.BMJ (Clinical research ed.) · 2024Article
- Genetic predictors of traits in elderly subjects: risk of survival bias and reverse causation.European heart journal · 2024Article
- Assessing the performance of physician's prescribing preference as an instrumental variable in comparative effectiveness research with moderate and small sample sizes: a simulation study.Journal of comparative effectiveness research · 2024Article
- Evaluating colonoscopy screening intervals in patients with Lynch syndrome from a large Canadian registry.Journal of the National Cancer Institute · 2023Article
- Cox regression can be collapsible and Aalen regression can be non-collapsible.Lifetime data analysis · 2023Article
- Instrumental variable estimation of the marginal structural Cox model for time-varying treatments.Biometrika · 2023Article
- Impact of waiting time on post-transplant survival for recipients with hepatocellular carcinoma: A natural experiment randomized by blood group.JHEP reports : innovation in hepatology · 2023Article
12 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
Bias due to unobserved confounding can seldom be ruled out with certainty when estimating the causal effect of a nonrandomized treatment. The instrumental variable (IV) design offers, under certain assumptions, the opportunity to tame confounding bias, without directly observing all confounders. The IV approach is very well developed in the context of linear regression and also for certain generalized linear models with a nonlinear link function. However, IV methods are not as well developed for regression analysis with a censored survival outcome. In this article, we develop the IV approach for regression analysis in a survival context, primarily under an additive hazards model, for which we describe 2 simple methods for estimating causal effects. The first method is a straightforward 2-stage regression approach analogous to 2-stage least squares commonly used for IV analysis in linear regression. In this approach, the fitted value from a first-stage regression of the exposure on the IV is entered in place of the exposure in the second-stage hazard model to recover a valid estimate of the treatment effect of interest. The second method is a so-called control function approach, which entails adding to the additive hazards outcome model, the residual from a first-stage regression of the exposure on the IV. Formal conditions are given justifying each strategy, and the methods are illustrated in a novel application to a Mendelian randomization study to evaluate the effect of diabetes on mortality using data from the Health and Retirement Study. We also establish that analogous strategies can also be used under a proportional hazards model specification, provided the outcome is rare over the entire follow-up.
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