DynTxRegime-package: Methods for Estimating Dynamic Treatment Regimes

In DynTxRegime: Methods for Estimating Dynamic Treatment Regimes

Description

Implementations of Interactive Q-Learning, Q-Learning, and value-search methods based on augmented inverse probability weighted estimators and inverse probability weighted estimators.

Details

Package:	DynTxRegime
Type:	Package
Version:	2.1
Date:	2015-06-10
License:	GPL-2
Depends:	methods, modelObj, rgenoud

Please see the references below for details of each method implemented.

Author(s)

Marie Davidian, Eric B. Laber, Kristin A. Linn, Leonard A. Stefanski, Anastasios A. Tsiatis, Baqun Zhang, Min Zhang, and Shannon T. Holloway
Maintainer: Shannon T. Holloway <[e-mail]>

References

Laber, E. B., Linn, K. A., and Stefanski, L. A. (2014). Interactive Q-learning. Biometrika, in press.

Zhang, B., Tsiatis, A. A., Davidian, M., Zhang, M., and Laber, E. B. (2012). Estimating Optimal Treatment Regimes from a Classification Perspective. Stat, 1, 103–114

Zhang, B., Tsiatis, A. A., Laber, E. B., and Davidian, M. (2012). A Robust Method for Estimating Optimal Treatment Regimes. Biometrics, 68, 1010–1018.

Zhang, B., Tsiatis, A. A., Laber, E. B., and Davidian, M. (2013) Robust Estimation of Optimal Dynamic Treatment Regimes for Sequential Treatment Decisions. Biometrika, 100, 681–694.

Mebane, W. and Sekhon, J. S. (2011). Genetic Optimization Using Derivatives : The rgenoud package for R. Journal of Statistical Software, 42, 1–26.

See Also

iqLearnSS, iqLearnFSM, iqLearnFSC, iqLearnFSV, optimalSeq, optimalClass, qLearn

DynTxRegime documentation built on May 2, 2019, 5:21 p.m.