DynTxRegime (version 2.1)

DynTxRegime-package: 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.

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