title: Marginal structural models and causal inference in epidemiology.
abstract: In observational studies with exposures or treatments that vary over time, standard approaches for adjustment of confounding are biased when there exist time-dependent confounders that are also affected by previous treatment. This paper introduces marginal structural models, a new class of causal models that allow for improved adjustment of confounding in those situations. The parameters of a marginal structural model can be consistently estimated using a new class of estimators, the inverse-probability-of-treatment weighted estimators.
authors.affiliation: Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
authors.forename: J M
authors.initials: JM
authors.lastname: Robins
authors.forename: M A
authors.initials: MA
authors.lastname: Hernán
authors.forename: B
authors.initials: B
authors.lastname: Brumback
mesh_terms: D019380:Anti-HIV Agents / Q000627:therapeutic use; D015984:Causality; D015986:Confounding Factors, Epidemiologic; D004812:Epidemiologic Methods; D015658:HIV Infections / Q000188:drug therapy / Q000401:mortality; D006801:Humans; D015233:Models, Statistical; D012307:Risk Factors; D013997:Time Factors; D015215:Zidovudine / Q000627:therapeutic use
chemical_list: D019380:Anti-HIV Agents; D015215:Zidovudine