Marginal Structural Models: unbiased estimation for longitudinal studies.
E. Moodie, und D. Stephens. International journal of public health, 56 (1):
117-9(Februar 2011)6250<m:linebreak></m:linebreak>GR: Canadian Institutes of Health Research/Canada; JID: 101304551; 2010/05/07 received; 2010/09/12 accepted; 2010/08/01 revised; 2010/10/08 aheadofprint; ppublish;<m:linebreak></m:linebreak>Propensity score; Marginal structural models; Introductori.
DOI: 10.1007/s00038-010-0198-4
Zusammenfassung
INTRODUCTION: In this article, we introduce Marginal Structural Models, which yield unbiased estimates of causal effects of exposures in the presence of time-varying confounding variables that also act as mediators. OBJECTIVES: We describe estimation via inverse probability weighting; estimation may also be accomplished by g-computation (Robins in Latent Variable Modeling and Applications to Causality, Springer, New York, pp 69-117, 1997; van der Wal et al. in Stat Med 28:2325-2337, 2009) or targeted maximum likelihood (Rosenblum and van der Laan in Int J Biostat 6, 2010). CONCLUSIONS: When both time-varying confounding and mediation are present in a longitudinal setting data, Marginal Structural Models are a useful tool that provides unbiased estimates.
%0 Journal Article
%1 Moodie2011
%A Moodie, Erica E M
%A Stephens, D A
%D 2011
%J International journal of public health
%K Bias(Epidemiology) ConfoundingFactors(Epidemiology) DataInterpretation EpidemiologicResearchDesign Humans LikelihoodFunctions LongitudinalStudies LongitudinalStudies:standards LongitudinalStudies:statistics&numericaldata Models Probability Statistical TimeFactors
%N 1
%P 117-9
%R 10.1007/s00038-010-0198-4
%T Marginal Structural Models: unbiased estimation for longitudinal studies.
%U http://www.ncbi.nlm.nih.gov/pubmed/20931349
%V 56
%X INTRODUCTION: In this article, we introduce Marginal Structural Models, which yield unbiased estimates of causal effects of exposures in the presence of time-varying confounding variables that also act as mediators. OBJECTIVES: We describe estimation via inverse probability weighting; estimation may also be accomplished by g-computation (Robins in Latent Variable Modeling and Applications to Causality, Springer, New York, pp 69-117, 1997; van der Wal et al. in Stat Med 28:2325-2337, 2009) or targeted maximum likelihood (Rosenblum and van der Laan in Int J Biostat 6, 2010). CONCLUSIONS: When both time-varying confounding and mediation are present in a longitudinal setting data, Marginal Structural Models are a useful tool that provides unbiased estimates.
%@ 1661-8564; 1661-8556
@article{Moodie2011,
abstract = {INTRODUCTION: In this article, we introduce Marginal Structural Models, which yield unbiased estimates of causal effects of exposures in the presence of time-varying confounding variables that also act as mediators. OBJECTIVES: We describe estimation via inverse probability weighting; estimation may also be accomplished by g-computation (Robins in Latent Variable Modeling and Applications to Causality, Springer, New York, pp 69-117, 1997; van der Wal et al. in Stat Med 28:2325-2337, 2009) or targeted maximum likelihood (Rosenblum and van der Laan in Int J Biostat 6, 2010). CONCLUSIONS: When both time-varying confounding and mediation are present in a longitudinal setting data, Marginal Structural Models are a useful tool that provides unbiased estimates.},
added-at = {2023-02-03T11:44:35.000+0100},
author = {Moodie, Erica E M and Stephens, D A},
biburl = {https://www.bibsonomy.org/bibtex/24d33221443b91f0f676cb461d9026282/jepcastel},
city = {Department of Epidemiology and Biostatistics, McGill University, 1020 Pine Avenue West, Montreal, QC, H3A 1A2, Canada. erica.moodie@mcgill.ca},
doi = {10.1007/s00038-010-0198-4},
interhash = {4ed78610d98336854c564aad55a77f64},
intrahash = {4d33221443b91f0f676cb461d9026282},
isbn = {1661-8564; 1661-8556},
issn = {1661-8564},
journal = {International journal of public health},
keywords = {Bias(Epidemiology) ConfoundingFactors(Epidemiology) DataInterpretation EpidemiologicResearchDesign Humans LikelihoodFunctions LongitudinalStudies LongitudinalStudies:standards LongitudinalStudies:statistics&numericaldata Models Probability Statistical TimeFactors},
month = {2},
note = {6250<m:linebreak></m:linebreak>GR: Canadian Institutes of Health Research/Canada; JID: 101304551; 2010/05/07 [received]; 2010/09/12 [accepted]; 2010/08/01 [revised]; 2010/10/08 [aheadofprint]; ppublish;<m:linebreak></m:linebreak>Propensity score; Marginal structural models; Introductori},
number = 1,
pages = {117-9},
pmid = {20931349},
timestamp = {2023-02-03T11:44:35.000+0100},
title = {Marginal Structural Models: unbiased estimation for longitudinal studies.},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20931349},
volume = 56,
year = 2011
}