c(person("Xinyu", "Tang", role = c("aut", "cre"),
email = "xtang@uams.edu"),
person("Maria", "Melguizo", role = "aut"))
Estimation and comparison of survival distributions of dynamic treatment regimes (DTRs) from sequentially randomized clinical trials

c(person("Xinyu", "Tang", role = c("aut", "cre"),
email = "xtang@uams.edu"),
person("Maria", "Melguizo", role = "aut",
email = "msmelguizo@uams.edu"))
survival, ggplot2, grid, aod, proto, R (>= 3.0.2)
Xinyu Tang [aut, cre],
Maria Melguizo [aut]

survival, ggplot2, gridExtra, aod, proto, R (>= 3.1.0)

Estimation and Comparison of Dynamic Treatment Regimes
survival, ggplot2, grid, gridExtra, aod, proto
Estimation and comparison of survival distributions of dynamic treatment regimes (DTRs) from sequentially randomized clinical trials.

Learning Algorithms for Dynamic Treatment Regimes
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.

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.

Statistical Learning Methods for Optimizing Dynamic Treatment
Regimes
Yuan Chen, Ying Liu, Donglin Zeng, Yuanjia Wang
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.