Developing adaptive treatment strategies in substance abuse research.

By Susan A. Murphy, K. Lynch, David W Oslin, J. McKay, T. Tenhave. 2007

209 citations recorded by the source corpus.

Subfields: Epidemiology, Statistics and Probability

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Citations in the corpus

Reinforcement Learning in Healthcare: A Survey (2019, methodology): “Utilizing valid data generated, for instance, from the Sequential Multiple Assignment Randomized Trial (SMART) [78], [79], an optimal DTR that is capable of optimizing the final clinical outcome of particular interest can be derived.”
TREE-BASED REINFORCEMENT LEARNING FOR ESTIMATING OPTIMAL DYNAMIC TREATMENT REGIMES (2018, background): “However, individuals with SUD are vastly heterogeneous in the course of this disorder, as well as in how they respond to different interventions (Murphy et al., 2007).”
Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research (2014, methodology): “of data analyses for the common scientific aims in a SMART” of [1]—to conduct one RCT to evaluate the ORIGINAL RESEARCH”
A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves (2016, background): “…models (van der Laan and Petersen, 2007), doubly robust learning (Tsiatis, 2007; Zhao et al., 2015) with applications to longitudinal data analysis (Hernán et al., 2000), survival analysis (Lunceford et al., 2002), and adaptive treatment selections in clinical trials (Murphy et al., 2007a,b).”
Dynamic treatment regimes (2009, methodology): “This is a type of cohort effects (see Murphy et al. 2007).”
An Overview of Research and Evaluation Designs for Dissemination and Implementation (2017, background): “Practical modifications of the individual-level randomized clinical trial have been made to test for program or intervention effectiveness under realistic conditions in randomized field trials (40), as well as for interventions delivered at the group level (77), multilevel interventions (14, 15), co”
Adaptive Interventions in Child and Adolescent Mental Health (2016, background): “Adaptive interventions are also known as “adaptive treatment strategies” (Lavori & Dawson, 2008; Murphy et al., 2007a, 2007b), “dynamic treatment strategies” (Lavori & Dawson, 2014), or sometimes simply “treatment strategies” (Lavori et al., 2000).”
Reinforcement learning design for cancer clinical trials (2009, unknown): “Dynamic treatment regimes are recently emerging as a new paradigm for the treatment and long term management of chronic disease, and they have been utilized in some trials such as sequential multiple assignment randomized trials (SMART) [6] and drug and alcohol dependency studies [7].”
Designing a Pilot Sequential Multiple Assignment Randomized Trial for Developing an Adaptive Treatment Strategy (2012, unknown): “'— or using more sophisticated data analytic methods such as dynamic regime marginal structural model estimation [14], iterative minimization and G-estimation of the structural nested mean model [10] [12] [16], or Q-Learning ([4] [40] for software, see http://methcenter.psu.edu).”
Estimating Response-Maximized Decision Rules With Applications to Breastfeeding (2009, background): “Other work in the area includes consideration of randomization, sample size calculations, and variable selection (Lavori 2000; Dawson and Lavori 2002; Lavori and Dawson 2004; Murphy 2004; Murphy et al. 2007; Oetting, Levy, Weiss, and Murphy 2008).”
A Bayesian Nonparametic Approach for Estimating Individualized Treatment-Response Curves (2016, background): “…models (van der Laan and Petersen, 2007), doubly robust learning (Tsiatis, 2007; Zhao et al., 2015) with applications to longitudinal data analysis (Hernán et al., 2000), survival analysis (Lunceford et al., 2002), and adaptive treatment selections in clinical trials (Murphy et al., 2007a,b).”