Intensive Care Medicine

, Volume 42, Issue 4, pp 576–579 | Cite as

What’s new in the quantification of causal effects from longitudinal cohort studies: a brief introduction to marginal structural models for intensivists

What's New in Intensive Care


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Conflicts of interest

Authors have no conflict of interest to declare.

Supplementary material

134_2015_3919_MOESM1_ESM.docx (99 kb)
Supplementary material 1 (DOCX 99 kb)


  1. 1.
    Benson K, Hartz AJ (2000) A comparison of observational studies and randomized, controlled trials. N Engl J Med 342:1878–1886CrossRefPubMedGoogle Scholar
  2. 2.
    Bailly S, Bouadma L, Azoulay E, Orgeas MG, Adrie C, Souweine B, Schwebel C, Maubon D, Hamidfar-Roy R, Darmon M, Wolff M, Cornet M, Timsit JF (2015) Failure of empirical systemic antifungal therapy in mechanically ventilated critically ill patients. Am J Respir Crit Care Med 191:1139–1146CrossRefPubMedGoogle Scholar
  3. 3.
    Robins JM, Hernan MA, Brumback B (2000) Marginal structural models and causal inference in epidemiology. Epidemiology 11:550–560CrossRefPubMedGoogle Scholar
  4. 4.
    Resche-Rigon M, Pirracchio R, Robin M, De Latour RP, Sibon D, Ades L, Ribaud P, Fermand JP, Thieblemont C, Socie G, Chevret S (2012) Estimating the treatment effect from non-randomized studies: the example of reduced intensity conditioning allogeneic stem cell transplantation in hematological diseases. BMC Blood Disord 12:10CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Gerhard T, Delaney JA, Cooper-Dehoff RM, Shuster J, Brumback BA, Johnson JA, Pepine CJ, Winterstein AG (2012) Comparing marginal structural models to standard methods for estimating treatment effects of antihypertensive combination therapy. BMC Med Res Methodol 12:119CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Bekaert M, Timsit JF, Vansteelandt S, Depuydt P, Vesin A, Garrouste-Orgeas M, Decruyenaere J, Clec’h C, Azoulay E, Benoit D (2011) Attributable mortality of ventilator-associated pneumonia: a reappraisal using causal analysis. Am J Respir Crit Care Med 184:1133–1139CrossRefPubMedGoogle Scholar
  7. 7.
    Rosenbaum PR, Rubin DB (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55CrossRefGoogle Scholar
  8. 8.
    Pirracchio R, Carone M, Rigon MR, Caruana E, Mebazaa A, Chevret S (2013) Propensity score estimators for the average treatment effect and the average treatment effect on the treated may yield very different estimates. Stat Methods Med Res. doi:10.1177/0962280213507034
  9. 9.
    Cole SR, Hernan MA (2008) Constructing inverse probability weights for marginal structural models. Am J Epidemiol 168:656–664CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Hernan MA, Robins JM (2006) Estimating causal effects from epidemiological data. J Epidemiol Community Health 60:578–586CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Hernan MA, Brumback B, Robins JM (2000) Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 11:561–570CrossRefPubMedGoogle Scholar
  12. 12.
    Do DP, Wang L, Elliott MR (2013) Investigating the relationship between neighborhood poverty and mortality risk: a marginal structural modeling approach. Soc Sci Med 91:58–66CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Yang S, Eaton CB, Lu J, Lapane KL (2014) Application of marginal structural models in pharmacoepidemiologic studies: a systematic review. Pharmacoepidemiol Drug Saf 23:560–571CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Muriel A, Penuelas O, Frutos-Vivar F, Arroliga A, Abraira V, Thille A, Brochard L, Nin N, Davies A, Amin P, Du B, Raymondos K, Rios F, Violi D, Maggiore S, Soares MA, Gonzalez M, Abroug F, Bulow H, Hurtado J, Kuiper M, Moreno R, Zeggwagh AA, Villagomez A, Jibaja M, Soto L, D’Empaire G, Matamis D, Koh Y, Anzueto A, Ferguson N, Esteban A (2015) Impact of sedation and analgesia during non-invasive positive pressure ventilation on outcome. A marginal structural model causal analysis. Intensive Care Med. doi:10.1007/s00134-015-3854-6
  15. 15.
    Munoz ID, van der Laan M (2012) Population intervention causal effects based on stochastic interventions. Biometrics 68:541–549CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg and ESICM 2015

Authors and Affiliations

  1. 1.U823Grenoble Alpes UniversityLa TroncheFrance
  2. 2.UMR 1137, IAME Team 5, DeSCID: Decision SCiences in Infectious Diseases, Control and Care Inserm/Paris DiderotSorbonne Paris Cité UniversityParisFrance
  3. 3.Department of Anesthesia and Perioperative Care, San Francisco General Hospital and Trauma CenterUniversity of California San FranciscoSan FranciscoUSA
  4. 4.Division of Biostatistics, School of Public HealthUniversity of California at BerkeleyBerkeleyUSA
  5. 5.Medical and Infectious Diseases ICU-Paris Diderot University/Bichat HospitalParisFrance

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