A design for testing clinical strategies: biased adaptive within‐subject randomization

By Philip W. Lavori, R. Dawson. 2000

227 citations recorded by the source corpus.

Subfields: Genetics, Statistics and Probability

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Citations in the corpus

Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data (2017, background): “The natural process of doctors making a sequence of intervention decisions adapted to the time-varying clinical status and features of a patient is coined as Dynamic treatment regimes (DTRs, [4]).”
Learning When-to-Treat Policies (2019, background): “The problem of learning optimal dynamic sequential decision rules is also called learning dynamic optimal regimes (Murphy, 2003; Robins, 2004), adaptive strategies (Lavori and Dawson, 2000), or batch off-policy policy learning in the reinforcement learning (RL) literature (Sutton and Barto, 2018).”
The International Journal of Biostatistics (2017, unknown): “…years, sequentially randomized trials have been recognized as being uniquely suited to the study of these exciting questions (Thall et al. (2000), Lavori and Dawson (2000), Lavori and Dawson (2004), Murphy (2005)), with researchers in other clinical areas also beginning to implement this design…”
Dynamic treatment regimes: technical challenges and applications (2014, methodology): “The Adaptive Pharmacological and Behavioral Treatments for Children with ADHD Trial (W. Pelham (PI); Nahum-Shani et al., 2012a; Lei et al., 2012) exemplifies a common SMART; we use this study for illustration.”
A Survey on Causal Inference (2020, methodology): “cteristics, the illness severity and other heterogeneity, aiming to get the optimal treatment strategy. These heterogeneities are called tailoring variables. To get a useful dynamic treatment regime, [72] introduces one ’biased coin adaptive within-subject’ (BCAWS) design. Then, [85] presents one ge”
A Pilot SMART for Developing an Adaptive Treatment Strategy for Adolescent Depression (2015, unknown): “ATSs can be developed and refined within the context of a sequential multiple assignment randomized trial (SMART; Lavori & Dawson, 2000, 2003; Murphy, 2005).”
Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens (2018, methodology): “To compare all the methods, we calculated the value function of the corresponding estimated optimal rule using expression (1) as the empirical average of a large independent test data set with a sample size of 20 000.”
Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes (2019, background): “These decision rules are alternatively known as adaptive treatment strategies [14, 15, 21, 35, 36] or treatment policies [18, 39, 40].”
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”
Adaptive Interventions in Child and Adolescent Mental Health (2016, background): “Several manuscripts in this special issue describe studies that utilize a SMART design (Lavori & Dawson, 2000, 2004; Murphy, 2005).”
Covariate‐adjusted adaptive randomization in a sarcoma trial with multi‐stage treatments (2005, methodology): “A review of the underlying issues in such settings, known as dynamic treatment regimes, is given by Lavori and Dawson [1].”
Optimal dynamic treatment regimes (2003, background): “Lavori and Dawson (2000) and Lavori et al. (2000) proposed that researchers implement sequentially randomized experiments to estimate optimal decisions rules (instead of sequential randomization they used the phrase biased adaptive within-subject randomization).”
Interactive Q-learning for Quantiles (2017, methodology): “Assumptions (C2)–(C3) hold by design when data are collected using a sequential multiple assignment randomized trial (SMART, Lavori and Dawson 2000, 2004; Murphy 2005b).”
New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes (2015, methodology): “It is critical to recognize that the presented approaches are developed for the discovery of optimal DTRs.”
Q-learning Residual Analysis: Application to The Effectiveness of Sequences of Antipsychotic Medications for Patients with Schizophrenia (2016, background): “Dynamic treatment regimes are also known as adaptive interventions [1] or adaptive treatment strategies [2].”