Instrumental Variable Additive Hazards Models

Li, Jialiang; Fine, Jason P.; Brookhart, Alan

doi:10.1111/biom.12244

Cited by 85 publications

(103 citation statements)

References 18 publications

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“…Several approaches for IV estimation of Cox proportional hazard regression models have been studied recently (see, for instance, Burne and Abrahamowicz (), Li et al . (), MacKenzie et al . (), Tchetgen Tchetgen et al .…”

Section: Discussionmentioning

confidence: 99%

An Instrumental Variable Procedure for Estimating Cox Models with Non-Proportional Hazards in the Presence Of Unmeasured Confounding

Martínez‐Camblor

MacKenzie

Staiger

et al. 2019

Journal of the Royal Statistical Society Series C: Applied Statistics

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Summary Two‐stage instrumental variable methods are commonly used for estimating average causal effects in the presence of an unmeasured confounder. In the context of the proportional hazard Cox regression models, this problem has recently received attention with several methods being proposed. Previously, we developed an improved estimator under the incumbent two‐stage residual inclusion procedure called ‘2SRI’ by adding a Gaussian frailty in the second stage. We now consider the more complex situation in which the treatment and the unmeasured confounders can have time varying effects, illustrating the method with the case of a step function with one prespecified change point. We prove that, in situations where the effects of the unmeasured confounder or the treatment change during the follow‐up, the first stage of the 2SRI algorithm induces a frailty with time varying coefficients in the second stage, which enables incumbent methods and our previously developed procedure to be improved on. A Monte Carlo simulation study demonstrates the superior performance of the proposed extension of 2SRI that we develop. We apply the new procedure to estimate the effect of endarterectomy versus carotid artery stenting on the time to death of patients suffering from carotid artery disease by using linked vascular quality initiative registry–Medicare data.

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Section: Discussionmentioning

confidence: 99%

An Instrumental Variable Procedure for Estimating Cox Models with Non-Proportional Hazards in the Presence Of Unmeasured Confounding

Martínez‐Camblor

MacKenzie

Staiger

et al. 2019

Journal of the Royal Statistical Society Series C: Applied Statistics

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“…We employ the same notations as Li, Fine and Brookhart (2015). Let T be the failure time of interest and C be the censoring time.…”

Section: Two-stage Predictor Substitution and Residual Inclusionmentioning

confidence: 99%

“… Summary Li, Fine and Brookhart (2015) presented an extension of the two-stage least squares (2SLS) method for additive hazards models which requires an assumption that the censoring distribution is unrelated to the endogenous exposure variable. We present another extension of 2SLS that can address this limitation.…”

mentioning

confidence: 99%

Reader Reaction: Instrumental Variable Additive Hazards Models with Exposure-Dependent Censoring

Chan

2016

Biometrics

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“…Li et al . () assumed linear structural equation models (SEMs) for the hazard function and developed a closed form, two‐stage estimator which can be applied for the causal effect of both continuous and discrete exposures. In this paper we are not limited to additive hazards.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Causal Effect of Treatment in Observational Studies with Survival Time End Points and Unmeasured Confounding

Choi

O’Malley

2016

Journal of the Royal Statistical Society Series C: Applied Statistics

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Summary Estimation of the effect of a treatment in the presence of unmeasured confounding is a common objective in observational studies. The Two Stage Least Squares (2SLS) Instrumental Variables (IV) procedure is frequently used but is not applicable to time-to-event data if some observations are censored. We develop a simultaneous equations model (SEM) to account for unmeasured confounding of the effect of treatment on survival time subject to censoring. The identification of the treatment effect is assisted by IVs (variables related to treatment but conditional on treatment not to the outcome) and the assumed bivariate distribution underlying the data generating process. The methodology is illustrated on data from an observational study of time to death following endovascular or open repair of ruptured abdominal aortic aneurysm. As the IV and the distributional assumptions cannot be jointly assessed from the observed data, we evaluate the sensitivity of the results to these assumptions.

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Instrumental Variable Additive Hazards Models

Cited by 85 publications

References 18 publications

An Instrumental Variable Procedure for Estimating Cox Models with Non-Proportional Hazards in the Presence Of Unmeasured Confounding

An Instrumental Variable Procedure for Estimating Cox Models with Non-Proportional Hazards in the Presence Of Unmeasured Confounding

Reader Reaction: Instrumental Variable Additive Hazards Models with Exposure-Dependent Censoring

Estimating the Causal Effect of Treatment in Observational Studies with Survival Time End Points and Unmeasured Confounding

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