%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 }