iqLearn Interactive strongQstrong-learning in R.pdf

iqLearn: Interactive Q-learning in R Kristin A. Linn, Eric B. Laber, Leonard A. Stefanski February 19, 2015 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 nonsmooth, 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. Keywords: Interactive Q-learning; Q-learning; Dynamic Treatment Regimes; Dynamic Pro- gramming; SMART design. 1 Introduction In practice, clinicians and intervention scientists must adapt treatment recommendations in response the uniquely evolving health status of each patient. Dynamic treatment regimes (DTRs) formalize this treatment process as a sequence of decision rules, one for each treat- ment decision, which map current and past patient information to a recommende