Research‎ > ‎Publications‎ > ‎

MSM References

For my publication list, click here. The following list is created for the beginners who might be interested in learning about MSM.

Marginal Structural Models (MSM) Reference List
Disclaimer: This page does not host any files listed below and is an index of links to other websites that may host the relevant files / information. Subscription from corresponding publishers may be required to access the files. Feel free to report broken links or a link that you think should be removed. This is not meant to be a comprehensive list.

Comprehensive book / chapters
  • Hernán M.Á., Robins J.M. Causal Inference, Chapman & Hall/CRC, 2015 (book-website, facebook-page)
  • Robins, James M., and Miguel A. Hernán. "Estimation of the causal effects of time-varying exposures." Longitudinal data analysis (2009): 553-599. (link, mirror, useful slides - part1, part2)
  • Robins J.M. Marginal structural models versus structural nested models as tools for causal inference. In Halloran, M. E. and Berry D., editors, Statistical Models in Epidemiology, the Environment and Clinical Trials,95–134, 1999. New York, NY: Springer-Verlag. (chapterproofs)
  • Robins, J. M., M. A. Hernán, and U. W. E. SiEBERT. "Effects of multiple interventions." Comparative quantification of health risks: global and regional burden of disease attributable to selected major risk factors 1 (2004): 2191-2230. (linkproofs)
  • Vittinghoff, Eric, Stephen Shiboski, and David V. Glidden Charles E. McCulloch. Strengthening Causal Inference. In Regression methods in biostatistics. New York:: Springer, 2012 (link).
Basic Understanding
  • Robins J.M., Hernán M.Á., and Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5):550–560, 2000. (link)
  • Hernán M.A. and Robins J.M. Estimating causal effects from epidemiological data. Journal of Epidemiology and Community Health, 60 (7):578–586, 2006. (link)
  • Maldonado, George, and Sander Greenland. "Estimating causal effects." International Journal of Epidemiology 31.2 (2002): 422-429. (link)
  • Daniel, R. M., et al. "Methods for dealing with time‐dependent confounding." Statistics in medicine 32.9 (2013): 1584-1618. (link)
  • Sato, Tosiya, and Yutaka Matsuyama. "Marginal structural models as a tool for standardization." Epidemiology 14.6 (2003): 680-686. (link)
Statistical theory
  • Robins JM. Marginal structural models. In: 1997 Proceedings of the American Statistical Association, Section on Bayesian Statistical Science. Alexandria, VA: American Statistical Association, 1998:1–10 (link)
  • Robins J.M. Association, causation, and marginal structural models. Synthese, 121(1):151–179, 1999. (link)
  • Robins J.M., Greenland S., and Hu F.C. Estimation of the causal effect of a time-varying exposure on the marginal mean of a repeated binary outcome. Journal of the American Statistical Association, 94(447):687–700, 1999. (link, rejoinder)
  • Hernán, Miguel A., Babette A. Brumback, and James M. Robins. "Estimating the causal effect of zidovudine on CD4 count with a marginal structural model for repeated measures." Statistics in medicine 21.12 (2002): 1689-1709. (link)
  • Hernán M.A., Brumback B., and Robins J.M. Marginal structural models to estimate the joint causal effect of nonrandomized treatments. Journal of the American Statistical Association, 96(454):440–448, 2001. (link)
  • Røysland, Kjetil. "A martingale approach to continuous-time marginal structural models." Bernoulli 17.3 (2011): 895-915. (link)
Directed acyclic graph (DAG)
  • Greenland S., Pearl J., and Robins J.M. Causal diagrams for epidemiologic research. Epidemiology, 10(1):37–48, 1999. (link)
  • Glymour M.M. Using causal diagrams to understand common problems in social epidemiology. In Oakes J.M. and Kaufman J.S., editors, Methods in Social Epidemiology. San Francesco, CA: Jossey-Bass/Wiley, 393-428, 2006. (link)
  • Hernán, Miguel A., Sonia Hernandez-Diaz, and James M. Robins. "A structural approach to selection bias." Epidemiology 15.5 (2004): 615-625. (link)
MSM Application guidelines
  • Mortimer K.M., Neugebauer R., Van der Laan M., and Tager I.B. An application of model-fitting procedures for marginal structural models. American Journal of Epidemiology, 162(4):382–388, 2005. (link)
  • Cole S.R. and Hernán M.A. Constructing inverse probability weights for marginal structural models. American Journal of Epidemiology, 168(6):656–664, 2008. (link)
  • Bodnar, Lisa M., et al. "Marginal structural models for analyzing causal effects of time-dependent treatments: an application in perinatal epidemiology." American Journal of Epidemiology 159.10 (2004): 926-934. (link)
  • Pullenayegum, Eleanor M., et al. "Fitting marginal structural models: estimating covariate-treatment associations in the reweighted data set can guide model fitting." Journal of clinical epidemiology 61.9 (2008): 875-881. (link)
Implementing as survey weights
  • Lumley, Thomas. Causal Inference. In Complex surveys: A guide to analysis using R. Vol. 565. John Wiley & Sons, 2011. (link, book-website)
  • Coffman D.L., Caldwell L.L., and Smith E.A. Introducing the at-risk average causal effect with application to HealthWise South Africa. Prevention Science, 13(4):437–447, 2012. (link)
  • Coffman D.L. and Zhong W. Assessing mediation using marginal structural models in the presence of confounding and moderation. Psychological Methods, 17(4):642–664, 2012. (link)
Data Analysis Examples
  • Hernán M.Á., Brumback B., and Robins J.M. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology, 11(5):561–570, 2000. (link, SAS code)
  • Cook N.R., Cole S.R., and Hennekens C.H. Use of a marginal structural model to determine the effect of aspirin on cardiovascular mortality in the Physicians’ Health Study. American Journal of Epidemiology, 155(11): 1045–1053, 2002. (link)
  • Cole S.R., Hernán M.A., Robins J.M., Anastos K., Chmiel J., Detels R., Ervin C., Feldman J., Greenblatt R., Kingsley L., Lai S., Young M., Cohen M., and Muñoz A. Effect of highly active antiretroviral therapy on time to acquired immunodeficiency syndrome or death using marginal structural models. American Journal of Epidemiology, 158(7): 687–694, 2003. (link)
  • VanderWeele, Tyler J., et al. "A marginal structural model analysis for loneliness: implications for intervention trials and clinical practice." Journal of consulting and clinical psychology 79.2 (2011): 225. (link)
Comparison with standard methods
  • Suarez, David, Roger Borras, and Xavier Basagaña. "Differences between marginal structural models and conventional models in their exposure effect estimates: a systematic review." Epidemiology 22.4 (2011): 586-588. (link)
  • Gerhard, Tobias, et al. "Comparing marginal structural models to standard methods for estimating treatment effects of antihypertensive combination therapy." BMC medical research methodology 12.1 (2012): 119. (link)
MSM Simulation Studies
  • Young, Jessica G., et al. "Simulation from structural survival models under complex time-varying data structures." JSM proceedings, section on statistics in epidemiology. American Statistical Association, Denver, CO (2008). (Abstract booklink)
  • Young J.G., Hernán M.A., Picciotto S., and Robins J.M. Relation between three classes of structural models for the effect of a time-varying exposure on survival. Lifetime Data Analysis, 16(1):71–84, 2010. (link, SAS codeStata-code)
  • Young, Jessica G., and Eric J. Tchetgen Tchetgen. "Simulation from a known Cox MSM using standard parametric models for the g‐formula." Statistics in medicine (2013). (link)
  • Xiao Y., Abrahamowicz M., and Moodie E.E.M. Accuracy of conventional and marginal structural Cox model estimators: A simulation study. The International Journal of Biostatistics, 6(2):1–28, 2010. (linkrelated thesis)
  • Westreich D., Cole S.R., Schisterman E.F., and Platt R.W. A simulation study of finite-sample properties of marginal structural Cox proportional hazards models. Statistics in Medicine, 31(19):2098–2109, 2012. (link)
  • Havercroft W.G. and Didelez V. Simulating from marginal structural models with time-dependent confounding. Statistics in Medicine, 31(30): 4190–4206, 2012. (proof, link)
  • Bryan J., Yu Z., and van der Laan M.J. Analysis of longitudinal marginal structural models. Biostatistics, 5(3):361–380, 2004. (link, sample R code)
  • Ali R.A., Ali M.A., and Wei Z. On computing standard errors for marginal structural cox models. Lifetime data analysis, pages 1–26, 2013. doi: 10.1007/s10985-013-9255-7. (link)
  • Moodie, Erica EM, David A. Stephens, and Marina B. Klein. "A marginal structural model for multiple‐outcome survival data: assessing the impact of injection drug use on several causes of death in the Canadian Co‐infection Cohort." Statistics in medicine (2013). (link, supporting materials)
Software Tools
  • Fewell Z., Hernán M.A., Wolfe F., Tilling K., Choi H., and Sterne JA. Controlling for time-dependent confounding using marginal structural models. Stata Journal, 4(4):402–420, 2004. (link)
  • Faries, Douglas E., et al. Analysis of Longitudinal Observational Data Using Marginal Structural Models. In Analysis of observational health care data using SAS. SAS Institute, 2010. (book-websitelink to chaptermirror, sample code)
  • Atkinson, Elizabeth J., and Terry M. Therneau. "The Basics of Propensity Scoring and Marginal Structural Models Cynthia S. Crowson, Louis A. Schenck, Abigail B. Green." (2013). (link)
  • van der Wal, Willem M., and Ronald B. Geskus. "Ipw: an R package for inverse probability weighting." Journal of Statistical Software 43.i13 (2011). (linkrelated thesis, R package)
  • Karim, M. E.; Gustafson, P.; Petkau, J. "Generating survival data for fitting marginal structural Cox models using Stata", Stata Conference, San Diego, July 26–27, 2012 (link)
Extensions of MSM
  • Cole S.R., Hudgens M.G., Tien P.C., Anastos K., Kingsley L., Chmiel J.S., and Jacobson L.P. Marginal structural models for case-cohort study designs to estimate the association of antiretroviral therapy initiation with incident AIDS or death. American Journal of Epidemiology, 175(5):381–390, 2012. (link, correction)
  • Howe C.J., Cole S.R., Mehta S.H., and Kirk G.D. Estimating the effects of multiple time-varying exposures using joint marginal structural models: alcohol consumption, injection drug use, and HIV acquisition. Epidemiology, 23(4): 574–582, 2012. (link, SAS code)
  • Cole, Stephen R., et al. "Using marginal structural measurement-error models to estimate the long-term effect of antiretroviral therapy on incident AIDS or death." American journal of epidemiology 171.1 (2010): 113-122. (link)
Effect modification
  • Robins, James M., Miguel A. Hernán, and Andrea Rotnitzky. "Invited commentary: effect modification by time-varying covariates." American journal of epidemiology 166.9 (2007): 994-1002. (link)
  • Chiba, Yasutaka, Kenichi Azuma, and Jiro Okumura. "Marginal structural models for estimating effect modification." Annals of epidemiology 19.5 (2009): 298-303. (link)
  • Kang, Joseph, et al. "Causal inference of interaction effects with inverse propensity weighting, G‐computation and tree‐based standardization."Statistical Analysis and Data Mining: The ASA Data Science Journal (2014). (link)
Use of MSM to determine optimal time to start treatment
  • Ewings, Fiona M., et al. "Optimal CD4 Count for Initiating HIV Treatment: Impact of CD4 Observation Frequency and Grace Periods, and Performance of Dynamic Marginal Structural Models." Epidemiology (Cambridge, Mass.) 25.2 (2014): 194. (link, Stata code)
  • Cain, Lauren E., et al. "When to initiate combined antiretroviral therapy to reduce mortality and AIDS-defining illness in HIV-infected persons in developed countries: an observational study." Annals of Internal Medicine 154.8 (2011): 509-15. (link)
  • Cain, Lauren E., et al. "When to start treatment? A systematic approach to the comparison of dynamic regimes using observational data." The international journal of biostatistics 6.2 (2010). (link)
Estimating direct and indirect effects
  • VanderWeele, Tyler J. "Marginal structural models for the estimation of direct and indirect effects." Epidemiology 20.1 (2009): 18-26. (link)
IPW adjusted Kaplan-Meier curve
  • Cole S.R. and Hernán M.A. Adjusted survival curves with inverse probability weights. Computer Methods and Programs in Biomedicine, 75(1):45–49, 2004. (link)
  • Westreich D., Cole S.R., Tien P.C., Chmiel J.S., Kingsley L., Funk M.J., Anastos K., and Jacobson L.P. Time scale and adjusted survival curves for marginal structural Cox models. American Journal of Epidemiology, 171(6): 691–700, 2010. (link)
Sensitivity Anlysis
  • Brumback, Babette A., et al. "Sensitivity analyses for unmeasured confounding assuming a marginal structural model for repeated measures." Statistics in medicine 23.5 (2004): 749-767. (link)
  • Klungsøyr, Ole, et al. "Sensitivity analysis for unmeasured confounding in a marginal structural Cox proportional hazards model." Lifetime data analysis15.2 (2009): 278-294. (link)
Model mis-specification
  • Lefebvre Genevieve, Delaney Joseph AC, and Platt Robert W. Impact of mis-specification of the treatment model on estimates from a marginal structural model. Statistics in medicine, 27(18):3629–3642, 2008. (link)
  • Imai, Kosuke, and Marc Ratkovic. "Robust Estimation of Inverse Probability Weights for Marginal Structural Models." Technical report. (2013). (link, R package)
Positivity Assumption
  • Platt R.W., Delaney J.A.C., and Suissa S. The positivity assumption and marginal structural models: the example of warfarin use and risk of bleeding. European Journal of Epidemiology, 27(2):77–83, 2012. (link)
  • Petersen M.L., Porter K.E., Gruber S., Wang Y., and van der Laan M.J. Diagnosing and responding to violations in the positivity assumption. Statistical Methods in Medical Research, 21(1):31–54, 2012. (link; software)
  • Naimi, Ashley I., et al. "A comparison of methods to estimate the hazard ratio under conditions of time-varying confounding and nonpositivity." Epidemiology 22.5 (2011): 718. (link, letter to editor)
Truncation
  • Xiao Y., Abrahamowicz M., and Moodie E.E.M. Comparison of approaches to weight truncation for marginal structural Cox models. Epidemiological Methods, 2(1):1–20, 2013. (link, related thesis)
Alternate approaches to deal with time-dependent confounding (non-G-methods)
  • Gran J.M., Røysland K., Wolbers M., Didelez V., Sterne J.A.C., Ledergerber B., Furrer H., von Wyl V., and Aalen O.O. A sequential Cox approach for estimating the causal effect of treatment in the presence of time-dependent confounding applied to data from the Swiss HIV Cohort Study. Statistics in Medicine, 29(26):2757–2768, 2010. (link, related thesis)
  • Røysland, Kjetil, et al. "Analyzing direct and indirect effects of treatment using dynamic path analysis applied to data from the Swiss HIV Cohort Study." Statistics in medicine 30.24 (2011): 2947-2958. (link)
  • Schaubel, Douglas E., et al. "Estimating the effect of a time-dependent treatment by levels of an internal time-dependent covariate: Application to the contrast between liver wait-list and posttransplant mortality." Journal of the American Statistical Association 104.485 (2009). (link)
MSM Related Thesis / Report
    • van der Wal, W. M. "Causal modeling in epidemiological practice." Thesis. Faculty of Medicine, University of Amsterdam (2011). (link)
    • Gall, Christine. "Statistical models for estimating the effects of intermediate variables in the presence of time-dependent confounders." Thesis. Faculty of Mathematics and Physics, Institute of Medicine. Biometrics and medicine. Computer science (SNACK) (2011). (link)
    • Peter, Karin. Marginal structural models and causal inference. Diss. Master thesis, Department of Mathematics, Swiss Federal Institute of Technology Zurich, 2011. (link)
    • Lima, Charlotte. "Marginal Structural Models: applied to treatment for HIV infection." Thesis. Faculty of Mathematics and Natural Sciences. University of Oslo (2012). (link)
    • Lee, Kyung Min. "Marginal structural modeling in health services research.", Technical Report, BU SPH. (link)
    Useful regression modeling references
    • Harrell F.E. Regression Modeling Strategies: with Applications to Linear Models, Logistic Regression, and Survival Analysis. New York, NY: Springer-Verlag, 2001. (link)
    • D’Agostino R.B., Lee M.L., Belanger A.J., Cupples L.A., Anderson K., and Kannel W.B. Relation of pooled logistic regression to time dependent Cox regression analysis: the Framingham Heart Study. Statistics in Medicine, 9(12): 1501–1515, 1990. (link)
    • Therneau T.M. Extending the Cox Model. Technical report, Section of Biostatistics, Mayo Clinic, Rochester, 1998. (link)

    Also take a look at James Robins's bibliography page.