Marginal structural models and causal inference in epidemiology.

by: JM Robins, MA Hernán, B Brumback

Epidemiology (Cambridge, Mass.), Vol. 11, No. 5. (September 2000), pp. 550-560.

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摘要

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.

@article{citeulike:2807799, 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.}, address = {Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.}, author = {Robins, J. M. and Hern\'{a}n, M. A. and Brumback, B. }, citeulike-article-id = {2807799}, issn = {1044-3983}, journal = {Epidemiology (Cambridge, Mass.)}, keywords = {msm}, month = {September}, number = {5}, pages = {550--560}, posted-at = {2008-05-17 15:36:13}, priority = {2}, title = {Marginal structural models and causal inference in epidemiology.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/10955408}, volume = {11}, year = {2000} }

RIS record

TY - JOUR ID - citeulike:2807799 L3 - citeulike-article-id:2807799 TI - Marginal structural models and causal inference in epidemiology. JF - Epidemiology (Cambridge, Mass.) VL - 11 IS - 5 SP - 550 EP - 560 AD - Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA. SN - 1044-3983 N2 - 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. KW - msm AU - Robins, JM AU - Hernán, MA AU - Brumback, B PY - 2000/09// UR - http://view.ncbi.nlm.nih.gov/pubmed/10955408 ER -

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