Covariate‐adjusted adaptive randomization in a sarcoma trial with multi‐stage treatments

74 citations recorded by the source corpus.

How researchers see this paper

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

Informing sequential clinical decision-making through reinforcement learning: an empirical study (2010, background): “There has been a surge of interest in recent years within the medical community regarding the application of Reinforcement Learning (RL) techniques to optimize treatment policies (Thall and Wathan 2000; Ernst et al. 2006; Murphy et al. 2007; Guez et al. 2008; Zhao et al. 2009).”
Robust Hybrid Learning for Estimating Personalized Dynamic Treatment Regimens (2016, background): “They are also known as adaptive treatment strategies (Lavori & Dawson, 2000), multi-stage treatment strategies (Thall, Sung, & Estey, 2002; Thall & Wathen, 2005) and treatment policies (Lunceford, Davidian, & Tsiatis, 2002; Wahed & Tsiatis, 2004, 2006).”
Demystifying Optimal Dynamic Treatment Regimes (2007, background): “Design considerations for multicenter, sequentially randomized trials with adaptive randomization have been addressed in a Bayesian framework (Thall, Millikan, and Sung, 2000; Thall and Wathen, 2005).”
Dynamic treatment regimes (2009, background): “The term ‘dynamic treatment regimes’ is also known as ‘adaptive treatment strategies’ (Lavori and Dawson 2000; Murphy 2005a), ‘multi-stage treatment strategies’ (Thall et al. 2002; Thall and Wathen 2005), ‘treatment policies’ (Lunceford et al.”