LTMLE

Chapter
Part of the Springer Series in Statistics book series (SSS)

Abstract

Sequential decision making is a natural part of existence. Humans make a myriad of decisions each day, and many decisions are typically involved when considering a single particular goal amidst an unpredictable and uncertain environment. Any action could impact future states and, importantly, the options available later.

References

  1. H. Bang, J.M. Robins, Doubly robust estimation in missing data and causal inference models. Biometrics 61, 962–972 (2005)MathSciNetCrossRefMATHGoogle Scholar
  2. O. Bembom, M.J. van der Laan, Analyzing sequentially randomized trials based on causal effect models for realistic individualized treatment rules. Stat. Med. 27, 3689–3716 (2008)MathSciNetCrossRefGoogle Scholar
  3. S. Lendle, J. Schwab, M.L. Petersen, M.J. van der Laan, ltmle: an R package for implementing targeted minimum loss-based estimation for longitudinal data. J. Stat. Softw. 81(1) (2017)Google Scholar
  4. E.E.M. Moodie, T.S. Richardson, D.A. Stephens, Demystifying optimal dynamic treatment regimes. Biometrics 63(2), 447–455 (2007)MathSciNetCrossRefMATHGoogle Scholar
  5. S.A. Murphy, Optimal dynamic treatment regimes. J. R. Stat. Soc. Ser. B 65(2), 331–66 (2003)MathSciNetCrossRefMATHGoogle Scholar
  6. L. Orellana, A. Rotnitzky, J.M. Robins, Dynamic regime marginal structural mean models for estimation of optimal treatment regimes, part I: main content. Int. J. Biostat. 6(2), Article 8 (2010)Google Scholar
  7. M. Petersen, J. Schwab, S. Gruber, N. Blaser, M. Schomaker, M.J. van der Laan, Targeted maximum likelihood estimation for dynamic and static longitudinal marginal structural working models. J. Causal Inference 2(2), 147–185 (2014)CrossRefGoogle Scholar
  8. J.M. Robins, Robust estimation in sequentially ignorable missing data and causal inference models, in Proceedings of the American Statistical Association (2000)Google Scholar
  9. J.M. Robins, Optimal structural nested models for optimal sequential decisions, in Proceedings of the Second Seattle Symposium in Biostatistics: Analysis of Correlated Data (2004)Google Scholar
  10. J.M. Robins, L. Orellana, A. Rotnitzky, Estimation and extrapolation of optimal treatment and testing strategies. Stat. Med. 27, 4678–4721 (2008b)Google Scholar
  11. M.J. van der Laan, Causal effect models for intention to treat and realistic individualized treatment rules. Technical Report, Division of Biostatistics, University of California, Berkeley (2006a)Google Scholar
  12. 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
  13. M.J. van der Laan, M.L. Petersen, Causal effect models for realistic individualized treatment and intention to treat rules. Int. J. Biostat. 3(1), Article 3 (2007)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Health Care PolicyHarvard Medical SchoolBostonUSA
  2. 2.Division of Biostatistics and Department of StatisticsUniversity of California, BerkeleyBerkeleyUSA

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