iqLearn: Interactive Q-Learning in R
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2015-03-01
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| Series: | Journal of Statistical Software |
| Online Access: | http://www.jstatsoft.org/index.php/jss/article/view/2238 |
| _version_ | 1819232153687293952 |
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| author | Kristin A. Linn Eric B. Laber Leonard A. Stefanski |
| author_facet | Kristin A. Linn Eric B. Laber Leonard A. Stefanski |
| author_sort | Kristin A. Linn |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-23T11:56:20Z |
| format | Article |
| id | doaj.art-f70656754c074d2c843a77f34768ef8f |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-12-23T11:56:20Z |
| publishDate | 2015-03-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-f70656754c074d2c843a77f34768ef8f2022-12-21T17:48:04ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602015-03-0164112510.18637/jss.v064.i01842iqLearn: Interactive Q-Learning in RKristin A. LinnEric B. LaberLeonard A. StefanskiChronic 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.http://www.jstatsoft.org/index.php/jss/article/view/2238 |
| spellingShingle | Kristin A. Linn Eric B. Laber Leonard A. Stefanski iqLearn: Interactive Q-Learning in R Journal of Statistical Software |
| title | iqLearn: Interactive Q-Learning in R |
| title_full | iqLearn: Interactive Q-Learning in R |
| title_fullStr | iqLearn: Interactive Q-Learning in R |
| title_full_unstemmed | iqLearn: Interactive Q-Learning in R |
| title_short | iqLearn: Interactive Q-Learning in R |
| title_sort | iqlearn interactive q learning in r |
| url | http://www.jstatsoft.org/index.php/jss/article/view/2238 |
| work_keys_str_mv | AT kristinalinn iqlearninteractiveqlearninginr AT ericblaber iqlearninteractiveqlearninginr AT leonardastefanski iqlearninteractiveqlearninginr |