Evaluating multiple treatment courses in clinical trials.

By Peter F. Thall, R. Millikan, H. Sung. 2000

151 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

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Citations in the corpus

Dynamic treatment regimes (2009, methodology): “Thall and colleagues (Thall et al. 2000, 2002, 2007) developed several likelihood based methods, both Bayesian and frequentist, for selecting the best dynamic treatment regime.”
The International Journal of Biostatistics (2017, unknown): “The original results of Thall et al. (2000) focus on ﬁtting logistic regression models for the diﬀerent stage-speciﬁc factors of the likelihood.”
Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes (2019, background): “These decision rules are alternatively known as adaptive treatment strategies [12, 13, 19, 33, 34] or treatment policies [16, 37, 38].”
Demystifying Optimal Dynamic Treatment Regimes (2007, unknown): “Thall et al. (2000) produced a likelihood-based approach to analyze sequentially randomized trials for optimal regimes for prostate cancer treatment, where randomization probabilities changed as information from patients accrued.”
An experimental design for the development of adaptive treatment strategies (2005, background): “…2001), the CATIE trial for antipsychotic medications in patients with Schizophrenia (see http:// www.catie.unc.edu/schizophrenia/synopsis.html), STAR*D for treatment of depression(Rush et al., 2003; Lavori et al., 2001) and phase II trials at MD Anderson for treating cancer (Thall et al., 2000).”
Designing a Pilot Sequential Multiple Assignment Randomized Trial for Developing an Adaptive Treatment Strategy (2012, unknown): “[22] for more recent work.”
A Generalization Error for Q-Learning (2005, background): “For example there are currently a number of ongoing large clinical trials for chronic disorders in which, each time an individual relapses, the individual is re-randomized to one of several further treatments (Schneider et al., 2001; Fava et al., 2003; Thall et al., 2000).”
Reinforcement learning design for cancer clinical trials (2009, unknown): “[4] provided a statistical framework for multi-course clinical trials involving some modifications of the play-the-winner-and-drop-the-loser strategy.”
Developing adaptive treatment strategies in substance abuse research. (2007, methodology): “, 2006) along with methods that permit a greater variety of outcome variables (Thall et al., 2000) or time-to-an-event outcomes (Lunceford et al.”
Adaptive Interventions Treatment Modelling and Regimen Optimization Using Sequential Multiple Assignment Randomized Trials (Smart) and Q-Learning (2018, background): “For this purpose, numerous literature on clinical trial design employing experimental data are present [5, 29-32, 42, 43] that defines construction of sequential multiple assignment randomized trial (SMART) where a patient is randomized more than one time through all possible treatment options at ea”
Placebo‐free designs for evaluating new mental health treatments: the use of adaptive treatment strategies (2004, background): “Others have referred to such strategies as dynamic treatments [10] or multi-course therapies [11].”
Demand-side vs. supply-side technology policies: Hidden treatment and new empirical evidence on the policy mix (2015, background): “The problem is very well known in clinical research, when typically various compounds are administered to patients as a cure for the same disease (Thall et al., 2000), and in the alternative medicine/integrative medicine framework, which studies the interaction of alternative and integrative medicin”