Skip Navigation



American Journal of Epidemiology Advance Access published online on September 17, 2007
American Journal of Epidemiology, doi:10.1093/aje/kwm231
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
166/9/994    most recent
kwm231v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Robins, J. M.
Right arrow Articles by Rotnitzky, A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Robins, J. M.
Right arrow Articles by Rotnitzky, A.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

American Journal of Epidemiology © The Author 2007. Published by the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Invited Commentary: Effect Modification by Time-varying Covariates

James M. Robins1,2, Miguel A. Hernán1 and Andrea Rotnitzky2,3

1 Department of Epidemiology, Harvard School of Public Health, Boston, MA
2 Department of Biostatistics, Harvard School of Public Health, Boston, MA
3 Department of Economics, Universidad Di Tella, Buenos Aires, Argentina

Correspondence to Dr. Miguel A. Hernán, Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115 (e-mail: miguel_hernan{at}post.harvard.edu).

Received for publication November 21, 2006. Accepted for publication March 9, 2007.

Marginal structural models (MSMs) allow estimation of effect modification by baseline covariates, but they are less useful for estimating effect modification by evolving time-varying covariates. Rather, structural nested models (SNMs) were specifically designed to estimate effect modification by time-varying covariates. In their paper, Petersen et al. (Am J Epidemiol 2007;000:000–00) describe history-adjusted MSMs as a generalized form of MSM and argue that history-adjusted MSMs allow a researcher to easily estimate effect modification by time-varying covariates. However, history-adjusted MSMs can result in logically incompatible parameter estimates and hence in contradictory substantive conclusions. Here the authors propose a more restrictive definition of history-adjusted MSMs than the one provided by Petersen et al. and compare the advantages and disadvantages of using history-adjusted MSMs, as opposed to SNMs, to examine effect modification by time-dependent covariates.

causality; confounding factors (epidemiology); longitudinal studies; nested model; observational data; structural model; time-dependent covariate

Abbreviations: MSM, marginal structural model; SNM, structural nested model


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Am J EpidemiolHome page
R. W. Platt, E. F. Schisterman, and S. R. Cole
Time-modified Confounding
Am. J. Epidemiol., September 15, 2009; 170(6): 687 - 694.
[Abstract] [Full Text] [PDF]

Home page
 Am J EpidemiolHome page
M. L. Petersen and M. J. van der Laan
Petersen et al. Respond to "Effect Modification by Time-varying Covariates"
Am. J. Epidemiol., November 1, 2007; 166(9): 1003 - 1004.
[Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.