Methodological Challenges in Constructing Effective Treatment Sequences for Chronic Psychiatric Disorders

Murphy, Susan A.; Oslin, David W.; Rush, A. John; Zhu, Ji

doi:10.1038/sj.npp.1301241

Cited by 88 publications

(76 citation statements)

References 37 publications

(39 reference statements)

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“…tailoring variables) that in the regression function interact sufficiently with the treatment that different values of the tailoring variables correspond to differing best treatments. See Murphy, et al (2006) for an intuitive discussion using standard (linear) regression.…”

Section: Instance-based Reinforcement Learningmentioning

confidence: 99%

Constructing evidence-based treatment strategies using methods from computer science

Pineau

Bellemare

Rush

et al. 2007

Drug and Alcohol Dependence

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This paper details a new methodology, instance-based reinforcement learning, for constructing adaptive treatment strategies from randomized trials. Adaptive treatment strategies are operationalized clinical guidelines which recommend the next best treatment for an individual based on his/her personal characteristics and response to earlier treatments. The instance-based reinforcement learning methodology comes from the computer science literature, where it was developed to optimize sequences of actions in an evolving, time varying system. When applied in the context of treatment design, this method provides the means to evaluate both the therapeutic and diagnostic effects of treatments in constructing an adaptive treatment strategy. The methodology is illustrated with data from the STAR*D trial, a multi-step randomized study of treatment alternatives for individuals with treatment-resistant major depressive disorder.

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Section: Instance-based Reinforcement Learningmentioning

confidence: 99%

Constructing evidence-based treatment strategies using methods from computer science

Pineau

Bellemare

Rush

et al. 2007

Drug and Alcohol Dependence

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“…The development of drug resistance may eliminate an entire drug class (or classes), limiting the number of treatment options. However, genotypic mutations, which alter susceptibility to antiretroviral therapy, may in theory "cost" the virus in terms of its "fitness" or ability to replicate (Croteau et al, 1997;Deeks et al, 2001;Nijhuis et al, 2001), and some studies have shown that cessation of therapy results in reversion of viral quasi-species back to wild-type virus. These observations have led to the hypothesis that an interruption in therapy eliminates drug pressure, resulting in reversion of viral populations to a more susceptible viral quasi-species, thus improving chances that subsequent salvage regimens will be effective.…”

Section: Hiv Therapy and Structured Or Supervised Treatment Interruptionmentioning

confidence: 99%

“…Adaptive treatment strategiesare a potentially powerful approach to operationalizing clinical decision-making (e.g., Lavori and Dawson, 2003;Murphy et al, 2006). An adaptive treatment strategy is a set of sequential decision rules, each of which describes how and when the next step of treatment for a patient should proceed based on information on the patient in the form of tailoring variables, such as previous treatments received, response to those treatments, and adherence of the patient up to that point, with the goal of achieving some clinical outcome.…”

Section: Introductionmentioning

confidence: 99%

“…One emerging approach is to base formulation of adaptive treatment strategies on data collected under suitable experimental designs and analytical "learning" methods that synthesize the information into sequential decision rules (e.g. Collins et al, 2004;Lavori and Dawson, 2003;Murphy, 2005;Murphy et al, 2006). Another idea is to exploit mathematical modelsthat represent the patient as a "system" that is changing over time.…”

Section: Introductionmentioning

confidence: 99%

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Using mathematical modeling and control to develop structured treatment interruption strategies for HIV infection

Rosenberg

Davidian

Banks

2007

Drug and Alcohol Dependence

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The goal of this article is to suggest that mathematical models describing biological processes taking place within a patient over time can be used to design adaptive treatment strategies. We demonstrate using the key example of treatment strategies for human immunodeficiency virus Type-1 (HIV) infection. Although there has been considerable progress in management of HIV infection using highly active antiretroviral therapies, continuous treatment with these agents involves significant cost and burden, toxicities, development of drug resistance, and problems with adherence; these latter complications are of particular concern in substanceabusing individuals. This has inspired interest in structured or supervised treatment interruption (STI) strategies, which involve cycles of treatment withdrawal and re-initiation. We argue that the most promising STI strategies are adaptive treatment strategies. We then describe how biological mechanisms governing the interaction over time between HIV and a patient's immune system may be represented by mathematical models and how control methods applied to these models can be used to design adaptive STI strategies seeking to maintain long-term suppression of the virus. We advocate that, when such mathematical representations of processes underlying a disease or disorder are available, they can be an important tool for suggesting adaptive treatment strategies for clinical study.

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“…Both proposals constitute a Sequential Multiple Assignment Randomized Trial (SMART; Murphy 2005;Lavori and Dawson 2007;Murphy et al 2007c;Oetting et al 2007). Both SMART proposals have these features: 1) participants are randomized multiple times over the course of a trial-that is, at each critical decision point-in order to experimentally evaluate which is the most appropriate treatment at each critical decision point, and 2) all participants are followed through the end of the trial to provide investigators the opportunity to evaluate and compare a variety of longitudinal outcomes (not just time to relapse) 368 ALMIRALL ET AL.…”

Section: Moving Beyond the Standard Discontinuation Trial Designmentioning

confidence: 99%

SMARTer Discontinuation Trial Designs for Developing an Adaptive Treatment Strategy

Almirall

Compton

Rynn

et al. 2012

Journal of Child and Adolescent Psychopharmacology

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Objective: Developing evidenced-based practices for the management of childhood psychiatric disorders requires research studies that address how to treat children during both the acute phase of the disorder and beyond. Given the selection of a medication for acute treatment, discontinuation trials are used to evaluate the effects of treatment duration (e.g., time on medication) and/or maintenance strategies following successful acute-phase treatment. Recently, sequential multiple assignment randomized trials (SMART) have been proposed for use in informing sequences of critical clinical decisions such as those mentioned. The objective of this article is to illustrate how a SMART study is related to the standard discontinuation trial design, while addressing additional clinically important questions with similar trial resources. Method: The recently completed Child/Adolescent Anxiety Multimodal Study (CAMS), a randomized trial that examined the relative efficacy of three acute-phase treatments for pediatric anxiety disorders, along with a next logical step, a standard discontinuation trial design, is used to clarify the ideas. This example is used to compare the discontinuation trial design relative to the SMART design. Results: We find that the standard discontinuation trial can be modified slightly using a SMART design to yield high-quality data that can be used to address a wider variety of questions in addition to the impact of treatment duration. We discuss how this innovative trial design is ultimately more efficient and less costly than the standard discontinuation trial, and may result in more representative comparisons between treatments. Conclusions: Mental health researchers who are interested in addressing questions concerning the effects of continued treatment (for different durations) following successful acute-phase treatment should consider SMART designs in place of discontinuation trial designs in their research. SMART designs can be used to address these and other questions concerning individualized sequences of treatment, such as the choice of a rescue treatment in case of postacute phase relapse.

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Methodological Challenges in Constructing Effective Treatment Sequences for Chronic Psychiatric Disorders

Cited by 88 publications

References 37 publications

Constructing evidence-based treatment strategies using methods from computer science

Constructing evidence-based treatment strategies using methods from computer science

Using mathematical modeling and control to develop structured treatment interruption strategies for HIV infection

SMARTer Discontinuation Trial Designs for Developing an Adaptive Treatment Strategy

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