about
Invited commentary: Agent-based models for causal inference—reweighting data and theory in epidemiology.Think globally, act globally: An epidemiologist's perspective on instrumental variable estimation.Should patients with chronic disease be told to gain weight? The obesity paradox and selection bias.Counterpoint: epidemiology to guide decision-making: moving away from practice-free researchBounding the per-protocol effect in randomized trials: an application to colorectal cancer screening.Atazanavir exposure in utero and neurodevelopment in infants: a comparative safety study.Comparative effectiveness of dynamic treatment regimes: an application of the parametric g-formulaA Comparison of Agent-Based Models and the Parametric G-Formula for Causal Inference.Examining Bias in Studies of Statin Treatment and Survival in Patients With Cancer.The value of explicitly emulating a target trial when using real world evidence: an application to colorectal cancer screening.Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses.Learning how to improve healthcare delivery: the Swedish Quality Registers.Electronic medical records can be used to emulate target trials of sustained treatment strategies.Extending inferences from a randomized trial to a new target populationA causal framework for classical statistical estimands in failure-time settings with competing events
P50
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P50
description
Forscher
@de
chercheur
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investigador
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researcher
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ricercatore
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研究者
@zh
name
Miguel Hernán
@ast
Miguel Hernán
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Miguel Hernán
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Miguel Hernán
@nl
Miguel Hernán
@sl
type
label
Miguel Hernán
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Miguel Hernán
@en
Miguel Hernán
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Miguel Hernán
@nl
Miguel Hernán
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prefLabel
Miguel Hernán
@ast
Miguel Hernán
@en
Miguel Hernán
@es
Miguel Hernán
@nl
Miguel Hernán
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P106
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P19
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P569
1971-01-01T00:00:00Z