Structural Nested Cumulative Failure Time Models to Estimate the Effects of Interventions
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Publication:4648531
DOI10.1080/01621459.2012.682532zbMath1443.62393OpenAlexW2045006889WikidataQ37387493 ScholiaQ37387493MaRDI QIDQ4648531
Jessica G. Young, John H. Page, Sally Picciotto, Miguel A. Hernán, James M. Robins
Publication date: 9 November 2012
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: http://europepmc.org/articles/pmc3860902
Applications of statistics to biology and medical sciences; meta analysis (62P10) Reliability and life testing (62N05)
Related Items (5)
Identification, estimation and approximation of risk under interventions that depend on the natural value of treatment using observational data ⋮ Simulating longitudinal data from marginal structural models using the additive hazard model ⋮ Revisiting g-estimation of the effect of a time-varying exposure subject to time-varying confounding ⋮ Inverse Probability Weighted Estimation of Risk Under Representative Interventions in Observational Studies ⋮ Structural nested models and G-estimation: the partially realized promise
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- Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments
- A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
- Correcting for non-compliance in randomized trials using structural nested mean models
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