Dynamic treatment regimes

By Susan A. Murphy, D. Almirall. 2009

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Chao Yu
2. Jiming Liu
3. Shamim Nemati
4. Liuyi Yao
5. Yaliang Li
6. Zhixuan Chu
7. Jing Gao
8. Aidong Zhang

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, background): “are decision-making is to develop effective treatment regimes that can dynamically adapt to the varying clinical states and improve the long-term benefits of patients. Dynamic treatment regimes (DTRs) [72], [73], alternatively named as dynamic treatment policies [74], adaptive interventions [75], or ”
• Using Reinforcement Learning to Personalize Dosing Strategies in a Simulated Cancer Trial with High Dimensional Data (2017, background): “Largely inspired by Yufan Zhao, Kosorok, and Zeng (2009) and Yufan Zhao, Zeng, et al. (2011), I estimated optimal dynamic treatment regimes for a cancer clinical trial for an advanced generic cancer.”
• A Survey on Causal Inference (2020, background): “There also exist several surveys that discuss one category of the causal effect estimation methods, such as the survey of matching based methods [131], survey of tree-based and ensemble-based method [10], and the review of dynamic treatment regimes [25].”