Optimal Dynamic Treatment Rules

  • Alexander R. Luedtke
  • Mark J. van der Laan
Part of the Springer Series in Statistics book series (SSS)


Suppose we observe n independent and identically distributed observations of a time-dependent random variable consisting of baseline covariates, initial treatment and censoring indicator, intermediate covariates, subsequent treatment and censoring indicator, and a final outcome. For example, this could be data generated by a sequential RCT in which one follows up a group of subjects, and treatment assignment at two time points is sequentially randomized, where the probability of receiving treatment might be determined by a baseline covariate for the first-line treatment, and time-dependent intermediate covariate (such as a biomarker of interest) for the second-line treatment.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Vaccine and Infectious Disease DivisionFred Hutchinson Cancer Research CenterSeattleUSA
  2. 2.Division of Biostatistics and Department of StatisticsUniversity of California, BerkeleyBerkeleyUSA

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