Marginal structural models and causal inference in epidemiology.
J. Robins, M. Hernán, and B. Brumback. Epidemiology (Cambridge, Mass.), 11 (5):
550-60(September 2000)4777<m:linebreak></m:linebreak>LR: 20071114; GR: R01-AI32475/AI/NIAID NIH HHS/United States; JID: 9009644; 0 (Anti-HIV Agents); 30516-87-1 (Zidovudine); ppublish;<m:linebreak></m:linebreak>Causalitat; Dades longitudinals; Marginal structural models.
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.
%0 Journal Article
%1 Robins2000
%A Robins, J M
%A Hernán, M A
%A Brumback, B
%D 2000
%J Epidemiology (Cambridge, Mass.)
%K Anti-HIVAgents Anti-HIVAgents:therapeuticuse Causality ConfoundingFactors(Epidemiology) EpidemiologicMethods HIVInfections HIVInfections:drugtherapy HIVInfections:mortality Humans Models RiskFactors Statistical TimeFactors Zidovudine Zidovudine:therapeuticuse
%N 5
%P 550-60
%T Marginal structural models and causal inference in epidemiology.
%U http://www.ncbi.nlm.nih.gov/pubmed/10955408
%V 11
%X 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.
%@ 1044-3983
@article{Robins2000,
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.},
added-at = {2023-02-03T11:44:35.000+0100},
author = {Robins, J M and Hernán, M A and Brumback, B},
biburl = {https://www.bibsonomy.org/bibtex/26dada0f77077c5b4c132abc4307ade46/jepcastel},
city = {Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.},
interhash = {96f5b2bff705d96e25914a1ecb0f44de},
intrahash = {6dada0f77077c5b4c132abc4307ade46},
isbn = {1044-3983},
issn = {1044-3983},
journal = {Epidemiology (Cambridge, Mass.)},
keywords = {Anti-HIVAgents Anti-HIVAgents:therapeuticuse Causality ConfoundingFactors(Epidemiology) EpidemiologicMethods HIVInfections HIVInfections:drugtherapy HIVInfections:mortality Humans Models RiskFactors Statistical TimeFactors Zidovudine Zidovudine:therapeuticuse},
month = {9},
note = {4777<m:linebreak></m:linebreak>LR: 20071114; GR: R01-AI32475/AI/NIAID NIH HHS/United States; JID: 9009644; 0 (Anti-HIV Agents); 30516-87-1 (Zidovudine); ppublish;<m:linebreak></m:linebreak>Causalitat; Dades longitudinals; Marginal structural models},
number = 5,
pages = {550-60},
pmid = {10955408},
timestamp = {2023-02-03T11:44:35.000+0100},
title = {Marginal structural models and causal inference in epidemiology.},
url = {http://www.ncbi.nlm.nih.gov/pubmed/10955408},
volume = 11,
year = 2000
}