Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Marginal structural models and causal inference in epidemiology.

J M Robins1, M A Hernán, B Brumback

  • 1Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.

Epidemiology (Cambridge, Mass.)
|August 24, 2000
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

On the asymptotic validity of confidence sets for linear functionals of solutions to integral equations.

Biometrika·2025
Same author

Effectiveness of a bedaquiline, linezolid, clofazimine 'core' for multidrug-resistant TB.

IJTLD open·2025
Same author

Using negative controls to identify causal effects with invalid instrumental variables.

Biometrika·2025
Same author

Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data.

medRxiv : the preprint server for health sciences·2021
Same author

Lockdown measures and relative changes in the age-specific incidence of SARS-CoV-2 in Spain.

Epidemiology and infection·2020
Same author

Multiple robustness in factorized likelihood models.

Biometrika·2018

This study introduces marginal structural models to accurately adjust for time-dependent confounding in observational studies. These new causal models and inverse-probability-of-treatment weighted estimators improve confounding adjustment for time-varying treatments.

Area of Science:

  • Epidemiology
  • Causal Inference
  • Biostatistics

Background:

  • Standard confounding adjustment methods are biased in observational studies with time-varying treatments and time-dependent confounders.
  • Time-dependent confounders affected by prior treatment violate assumptions of traditional causal adjustment techniques.

Purpose of the Study:

  • To introduce marginal structural models for improved confounding adjustment in observational studies with time-varying treatments.
  • To present inverse-probability-of-treatment weighted estimators for consistent estimation of marginal structural model parameters.

Main Methods:

  • Development of marginal structural models as a new class of causal models.
  • Application of inverse-probability-of-treatment weighted (IPTW) estimators for parameter estimation.

Related Experiment Videos

  • Focus on observational studies with exposures or treatments that vary over time.

    • Main Results:

    • Marginal structural models provide unbiased adjustment for time-dependent confounding.
    • Inverse-probability-of-treatment weighted estimators yield consistent parameter estimates.
    • Demonstration of improved confounding adjustment in complex time-dependent scenarios.

    Conclusions:

    • Marginal structural models represent a significant advancement in causal inference for time-varying treatments.
    • IPTW estimators offer a robust method for analyzing data with time-dependent confounding.
    • These methods enhance the validity of observational studies investigating treatment effects over time.