LTMLE with Clustering

  • Mireille E. Schnitzer
  • Mark J. van der Laan
  • Erica E. M. Moodie
  • Robert W. Platt
Part of the Springer Series in Statistics book series (SSS)


Breastfeeding is considered best practice in early infant feeding, and is recommended by most major health organizations. However, due to the impossibility of directly allocating breastfeeding as a randomized intervention, no direct experimental evidence is available. The PROmotion of Breastfeeding Intervention Trial (PROBIT) was a cluster-randomized trial that sought to evaluate the effect of a hospital program that encouraged and supported breastfeeding, thereby producing indirect evidence of its protective effect on infant infections and hospitalizations.


  1. H. Bang, J.M. Robins, Doubly robust estimation in missing data and causal inference models. Biometrics 61, 962–972 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  2. A.C. Cameron, J.B. Gelbach, D.L. Miller, Boostrap-based improvements for inference with clustered errors. Rev. Econ. Stat. 90(3), 414–427 (2008)CrossRefGoogle Scholar
  3. M. Finster, M. Wood, The Apgar score has survived the test of time. Anesthesiology 102(4), 855–857 (2005)CrossRefGoogle Scholar
  4. S. Gruber, M.J. van der Laan, A targeted maximum likelihood estimator of a causal effect on a bounded continuous outcome. Int. J. Biostat. 6(1), Article 26 (2010b)Google Scholar
  5. T. Hastie, gam: generalized additive models (2011)
  6. M.G. Hudgens, M.E. Halloran, Toward causal inference with interference. J. Am. Stat. Assoc. 103(482), 832–842 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  7. M.S. Kramer, B. Chalmers, E.D. Hodnett, Z. Sevkovskaya, I. Dzikovich, S. Shapiro, J.P. Collet, I. Vanilovich, I. Mezen, T. Ducruet, G. Shishko, V. Zubovich, D. Mknuik, E. Gluchanina, V. Dombrovskiy, A. Ustinovitch, T. Kot, N. Bogdanovich, L. Ovchinikova, E. Helsing, PROmotion of breastfeeding intervention trial (PROBIT). J. Am. Med. Assoc. 285(4), 413–420 (2001)CrossRefGoogle Scholar
  8. M.S. Kramer, T. Guo, R.W. Platt, S. Shapiro, J.P. Collet, B. Chalmers, E. Hodnett, Z. Sevkovskaya, I. Dzikovich, I. Vanilovich, Breastfeeding and infant growth: biology or bias? Pediatrics 110(2), 343–347 (2002)CrossRefGoogle Scholar
  9. S. Milborrow, T Hastie, R Tibshirani, Earth: multivariate adaptive regression spline models. R package version 3.2-7 (2014)Google Scholar
  10. A. Peters, T. Hothorn, ipred: improved predictors (2009)
  11. E.C. Polley, M.J. van der Laan, SuperLearner: super learner prediction (2013).
  12. E.C Polley, S. Rose, M.J. van der Laan, Super-learning, in Targeted Learning: Causal Inference for Observational and Experimental Data, ed. by M.J. van der Laan, S. Rose (Springer, Berlin, Heidelberg, New York, 2011)Google Scholar
  13. K.E Porter, S. Gruber, M.J. van der Laan, J.S. Sekhon, The relative performance of targeted maximum likelihood estimators. Int. J. Biostat. 7(1) (2011)Google Scholar
  14. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna (2016).
  15. J.M. Robins, A new approach to causal inference in mortality studies with sustained exposure periods–application to control of the healthy worker survivor effect. Math. Modell. 7, 1393–1512 (1986)MathSciNetCrossRefzbMATHGoogle Scholar
  16. J.M. Robins, Robust estimation in sequentially ignorable missing data and causal inference models, in Proceedings of the American Statistical Association (2000)Google Scholar
  17. D.B. Rubin, Randomization analysis of experimental data: The fisher randomization test comment. J. Am. Stat. Assoc. 75(371), 591–593 (1980)Google Scholar
  18. M.E. Schnitzer, M.J. van der Laan, E.E.M. Moodie, R.W. Platt, Effect of breastfeeding on gastrointestinal infection in infants: a targeted maximum likelihood approach for clustered longitudinal data. Ann. Appl. Stat. 8(2), 703–725 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  19. M.J. van der Laan, S. Gruber, Targeted minimum loss based estimation of causal effects of multiple time point interventions. Int. J. Biostat. 8(1), Article 9 (2012)Google Scholar
  20. M.J. van der Laan, E.C. Polley, A.E. Hubbard, Super learner. Stat. Appl. Genet. Mol. 6(1), Article 25 (2007)Google Scholar
  21. T.J. VanderWeele, M.A. Hernán, Causal inference under multiple versions of treatment. J. Causal Inference 1(1), 1–20 (2013)CrossRefGoogle Scholar
  22. W.N. Venables, B.D. Ripley, Modern Applied Statistics with S, 4th edn. (Springer, Berlin, Heidelberg, New York, 2002)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Mireille E. Schnitzer
    • 1
  • Mark J. van der Laan
    • 2
  • Erica E. M. Moodie
    • 3
  • Robert W. Platt
    • 3
  1. 1.Faculté de pharmacie, Université de Montréal, #2236chemin de la Polytechnique, MontrealCanada
  2. 2.Division of Biostatistics and Department of Statistics, University of California, BerkeleyBerkeleyUSA
  3. 3.Department of Epidemiology, Biostatistics, and Occupational Health, McGill UniversityMontrealCanada

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