Packaged: 2015-12-31 15:02:48 UTC; tangxinyu

Title: Learning Algorithms for Dynamic Treatment Regimes
Description: Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each stage by potentially time-varying patient features and intermediate outcomes observed in previous stages. There are 3 main type methods, O-learning, Q-learning and P-learning to learn the optimal Dynamic Treatment Regimes with continuous variables. This package provide these state of arts algorithms to learn DTRs.

Author: Ying Liu, Yuanjia Wang, Donglin Zeng
Description: Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each stage by time-varying subject-specific features and intermediate outcomes observed in previous stages. This package implements three methods: O-learning (Zhao et. al. 2012,2014), Q-learning (Murphy et. al. 2007; Zhao et.al. 2009) and P-learning (Liu et. al. 2014, 2015) to estimate the optimal DTRs.

Title: Statistical Learning Methods for Optimizing Dynamic Treatment
Author: Yuan Chen, Ying Liu, Donglin Zeng, Yuanjia Wang
Description: We provide a comprehensive software to estimate general K-stage DTRs from SMARTs with Q-learning and a variety of outcome-weighted learning methods. Penalizations are allowed for variable selection and model regularization. With the outcome-weighted learning scheme, different loss functions - SVM hinge loss, SVM ramp loss, binomial deviance loss, and L2 loss - are adopted to solve the weighted classification problem at each stage; augmentation in the outcomes is allowed to improve efficiency. The estimated DTR can be easily applied to a new sample for individualized treatment recommendations or DTR evaluation.
Depends: kernlab,MASS,Matrix,foreach,glmnet, R (>= 2.10)

Title: DTR Estimation and Inference via G-Estimation, Dynamic WOLS, and
Author: Michael Wallace, Erica E M Moodie and David A Stephens
Description: Dynamic treatment regime estimation and inference via G-estimation,
dynamic weighted ordinary least squares (dWOLS) and Q-learning. Inference
via bootstrap and (for G-estimation) recursive sandwich estimation.