Sequenced treatment alternatives to relieve depression (STAR*D): rationale and design.

By A. Rush, Maurizio Fava, S. Wisniewski, Philip W. Lavori, Madhukar H. Trivedi, H. Sackeim, M. Thase, A. Nierenberg, F. Quitkin, T. Kashner and 10 more. 2004

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Subfields: Experimental and Cognitive Psychology, Pharmacology, Psychiatry and Mental health

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Citations in the corpus

Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data (2017, background): “The most common data source for these multiple decision making problems is through sequential multiple assignment randomized trial (SMART) [5], [6], which have been proposed to optimally construct DTRs that offer causal interpretation through randomization at each critical decision point.”
Informing sequential clinical decision-making through reinforcement learning: an empirical study (2010, background): “Clinicians wanting to construct principled (i.e. evidence based) rules for tailoring treatment have begun to run sequential multiple assignment randomized trials (SMART) (Murphy 2005; Dawson and Lavori 2004; Rush et al. 2004; Thall and Wathan 2000).”
Estimating Response-Maximized Decision Rules With Applications to Breastfeeding (2009, background): “Notable exceptions include work in the mental health sciences (Schneider et al. 2001; Rush et al. 2003; Dawson and Lavori 2004) and in cancer (Thall et al. 2007).”
Report by the ACNP Task Force on Response and Remission in Major Depressive Disorder (2006, background): “…Network for Mood and Anxiety Treatements (CANMAT), 2001; Reesal and Lam, 2001; Bauer et al, 2002a, b; Hirschfeld et al, 1997; Ballenger, 1999; Trivedi et al, 1998; Crismon et al, 1999; Rush et al, 2003a, 2004; Fava et al, 2003b; the American Academy of Child and Adolescent Psychiatry, 1998).”
Tractable Objectives for Robust Policy Optimization (2012, neutral): “, CATIE (H = 2) [16, 17] and STAR*D (H = 3) [14].”
Adaptive Interventions in Child and Adolescent Mental Health (2016, background): “, 2005; Shortreed & Moodie, 2012) and the Sequenced Treatment Alternatives to Relieve Depression in depression (Lavori et al., 2001; Rush et al., 2004) are examples of early precursors to the SMART in adult mental health research.”
Difference in treatment outcome in outpatients with anxious versus nonanxious depression: a STAR*D report. (2008, methodology): “Broadly inclusive selection criteria were used (16, 17).”
The STAR*D project results: A comprehensive review of findings (2007, background): “Level 2 medication switch The medication switches at Level 2 were designed to compare medications with different pharmacologic effects (ie, sertraline, a second SSRI; bupropion-SR, a nonserotonin active agent; venlafaxine-XR, a reuptake blocker of both norepinephrine and serotonin) [19, 20 ].”
Constructing evidence-based treatment strategies using methods from computer science. (2007, methodology): “We then provide early results from its use in constructing treatment strategies from a recently completed SMART study called the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial (www.star-d.org) (Fava et al. 2003;Rush et al. 2004).”
Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. (2006, methodology): “The protocol (7, 36) required a fully adequate dose of citalopram for a sufficient time to ensure that the likelihood of achieving remission was maximized and that those who did not reach remission were truly resistant to the medication.”
Demystifying Optimal Dynamic Treatment Regimes (2007, positive): “Alongside the theoretical innovations, within-person sequentially randomized trials are being performed for treatment of mental illness (Schneider et al., 2001; Rush et al., 2003) and cancer (Thall et al.”
Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis (2010, background): “This mimics a simplified version of STAR*D, of one of the largest randomized trial datasets currently available (Rush et al., 2004).”
Interactive model building for Q-learning (2014, methodology): “We use a measure of efficacy, the Quick Inventory of Depression Symptomatology score, as the outcome (Rush et al., 2004); side-effects or other competing outcomes could be accommodated using set-valued treatment regimes (Lizotte et al., 2012; Laber et al., 2013) or composite outcomes (Wang et al.,…”