Marginal structural models and causal inference in epidemiology.
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.
Robins
J M
JM
Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
Hernán
M A
MA
Brumback
B
B
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
D019380:Anti-HIV Agents; D015215:Zidovudine