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iqLearn: Interactive Q-Learning in R

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  • Linn, Kristin A.
  • Laber, Eric B.
  • Stefanski, Leonard A.

Abstract

Chronic illness treatment strategies must adapt to the evolving health status of the patient receiving treatment. Data-driven dynamic treatment regimes can offer guidance for clinicians and intervention scientists on how to treat patients over time in order to bring about the most favorable clinical outcome on average. Methods for estimating optimal dynamic treatment regimes, such as Q-learning, typically require modeling non- smooth, nonmonotone transformations of data. Thus, building well-fitting models can be challenging and in some cases may result in a poor estimate of the optimal treatment regime. Interactive Q-learning (IQ-learning) is an alternative to Q-learning that only requires modeling smooth, monotone transformations of the data. The R package iqLearn provides functions for implementing both the IQ-learning and Q-learning algorithms. We demonstrate how to estimate a two-stage optimal treatment policy with iqLearn using a generated data set bmiData which mimics a two-stage randomized body mass index reduction trial with binary treatments at each stage.

Suggested Citation

  • Linn, Kristin A. & Laber, Eric B. & Stefanski, Leonard A., 2015. "iqLearn: Interactive Q-Learning in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i01).
    • Handle: RePEc:jss:jstsof:v:064:i01
    DOI: http://hdl.handle.net/10.18637/jss.v064.i01
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    References listed on IDEAS

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    1. Bibhas Chakraborty & Eric B. Laber & Yingqi Zhao, 2013. "Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m-Out-of-n Bootstrap Scheme," Biometrics, The International Biometric Society, vol. 69(3), pages 714-723, September.
    2. Richard Bellman, 1957. "On a Dynamic Programming Approach to the Caterer Problem--I," Management Science, INFORMS, vol. 3(3), pages 270-278, April.
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