ArticleJournal of the American Statistical Association2024
Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.
Article in Journal of the American Statistical Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- FA-MFMR: a multivariable functional Mendelian randomization method that accounts for correlated longitudinal exposures via factor augmentation.Bioinformatics (Oxford, England) · 2026Article
- Instrumental variable approaches for estimating time-varying treatment effects in comparative effectiveness research.BMC medical research methodology · 2025Article
- Instrumental Variable Methods to Target Hypothetical Estimands With Longitudinal Repeated Measures Data: Application to the STEP 1 Trial.Statistics in medicine · 2025Article
- Estimating Time-Varying Exposure Effects Through Continuous-Time Modelling in Mendelian Randomization.Statistics in medicine · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Robins introduced Marginal Structural Models (MSMs), a general class of counterfactual models for the joint effects of time-varying treatment regimes in complex longitudinal studies subject to time-varying confounding. In his work, identification of MSM parameters is established under a Sequential Randomization Assumption (SRA), which rules out unmeasured confounding of treatment assignment over time. We consider sufficient conditions for identification of the parameters of a subclass, Marginal Structural Mean Models (MSMMs), when sequential randomization fails to hold due to unmeasured confounding, using instead a time-varying instrumental variable. Our identification conditions require that no unobserved confounder predicts compliance type for the time-varying treatment. We describe a simple weighted estimator and examine its finite-sample properties in a simulation study. We apply the proposed estimator to examine the effect of delivery hospital type on neonatal survival probability. Supplementary materials for this article are available online.
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Registered trials
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