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Optimal dynamic treatment regimes
A dynamic treatment regime is a list of decision rules, one per time interval, for how the level of treatment will be tailored through time to an individual's changing status. The goal of this paperExpand
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Because many illnesses show heterogeneous response to treatment, there is increasing interest in individualizing treatment to patients [11]. An individualized treatment rule is a decision rule thatExpand
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The prevention and treatment of missing data in clinical trials.
Missing data in clinical trials can have a major effect on the validity of the inferences that can be drawn from the trial. This article reviews methods for preventing missing data and, failing that,Expand
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An experimental design for the development of adaptive treatment strategies.
  • S. Murphy
  • Medicine
  • Statistics in medicine
  • 30 May 2005
In adaptive treatment strategies, the treatment level and type is repeatedly adjusted according to ongoing individual response. Since past treatment may have delayed effects, the development of theseExpand
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Consistency in a Proportional Hazards Model Incorporating a Random Effect
Supported in part by the North Atlantic Treaty Organization under a grant awarded in 1990 & conducted at the Mathematical Institute of the University of Utrecht, The Netherlands
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Asymptotic Theory for the Frailty Model
The frailty model is a generalization of Cox's proportional hazards model which includes a random effect. Nielsen, Gill, Andersen and Sorensen (1992) proposed an EM algorithm to estimate theExpand
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A Conceptual Framework for Adaptive Preventive Interventions
Recently, adaptive interventions have emerged as a new perspective on prevention and treatment. Adaptive interventions resemble clinical practice in that different dosages of certain prevention orExpand
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The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions.
In this article two new methods for building and evaluating eHealth interventions are described. The first is the Multiphase Optimization Strategy (MOST). It consists of a screening phase, in whichExpand
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A Generalization Error for Q-Learning
  • S. Murphy
  • Computer Science, Medicine
  • J. Mach. Learn. Res.
  • 1 December 2005
We consider Q-learning with function approximation for this setting and derive an upper bound on the generalization error. Expand
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