DynTxRegime (version 3.01)

DynTxRegime-package:

Description

Implementations of Interactive Q-Learning, Q-Learning, value-search methods based on augmented inverse probability weighted estimators and inverse probability weighted estimators, outcome weighted learning (OWL), residual weighted learning (RWL), backward outcome weighted learning (BOWL), and efficient augmentation and relaxation learning (EARL).

Details

Package:	DynTxRegime
Type:	Package
Version:	3.01
Date:	2017-05-21
License:	GPL-2
Depends:	methods, modelObj, stats
Suggests:	MASS, rpart, nnet

See the references below for details of each method implemented.

References

Laber, E. B., Linn, K. A., and Stefanski, L. A. (2014). Interactive model building for Q-learning. Biometrika, 101, 831--847.

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.

Zhao, Y-Q., Laber, E. B., Saha, S., and Sands, B. E. (2016+). Efficient Augmentation and Relaxation Learning for Treatment Regimes Using Observational Data. in press.

Zhou, X., Mayer-Hamblett, N., Kham, U., and Kosorok, M. R. (2016+). Residual Weighted Learning for Estimating Individualized Treatment Rules. Journal of the American Statistical Association, in press.

Zhao, Y-Q., Zeng, D., Rush, A. J., and Kosrok, M. R. (2012). Estimating Individualized Treatment Rules Using Outcome Weighted Learning. Journal of the American Statistical Association, 107, 1106--1118.

Zhao, Y-Q., Zeng, D., Laber, E. B., and Kosorok, M. R. (2015). New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes. Journal of the American Statistical Association, 110, 583--598.

See Also

bowl, earl, iqLearnFSC, iqLearnFSM, iqLearnFSV, iqLearnSS, optimalClass, optimalSeq, owl, qLearn, and rwl,