A SMART data analysis method for constructing adaptive treatment strategies for substance use disorders

Nahum‐Shani, Inbal; Ertefaie, Ashkan; Xi, Lü; Lynch, Kevin G.; Oslin, David W.; Almirall, Daniel

doi:10.1111/add.13743

Cited by 42 publications

(31 citation statements)

References 45 publications

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“…All the example experimental designs discussed earlier are like this in that the adaptive intervention in Figure 1, which resembles typical practice for children with autism (Kasari & Smith, 2016; Smith, 2010), is embedded within each of them. Another example is that we may be more willing, in optimization studies, to focus on reducing Type II errors (false negatives) than on controlling Type I errors (false positives), which is more typical of confirmatory trials (e.g., Nahum-Shani et al, 2017). …”

Section: Optimization Studies May Involve Different Considerations Asmentioning

confidence: 99%

Developing Optimized Adaptive Interventions in Education

Almirall

Kasari

McCaffrey

et al. 2017

Journal of Research on Educational Effectiveness

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Hedges (2018) encourages us to consider asking new scientific questions concerning the optimization of adaptive interventions in education. In this commentary, we have expanded on this (albeit briefly) by providing concrete examples of scientific questions and associated experimental designs to optimize adaptive interventions, and commenting on some of the ways such designs might challenge us to think differently. A great deal of methodological work remains to be done. For example, we have only begun to consider experimental design and analysis methods for developing “cluster-level adaptive interventions” (NeCamp, Kilbourne, & Almirall, 2017), or to extend methods for comparing the marginal mean trajectories between the adaptive interventions embedded in a SMART (Lu et al., 2016) to accommodate random effects. These methodological advances, among others, will propel educational research concerning the construction of more complex, yet meaningful, interventions that are necessary for improving student and teacher outcomes.

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Section: Optimization Studies May Involve Different Considerations Asmentioning

confidence: 99%

Developing Optimized Adaptive Interventions in Education

Almirall

Kasari

McCaffrey

et al. 2017

Journal of Research on Educational Effectiveness

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“…These findings support frequent assessment of craving in patients with AUD to inform lapse risk and treatment approach 81 . Future research may also consider including craving as a marker of treatment response within adaptive algorithms for personalised interventions 7,81,82 . Patients with persistent craving are likely to benefit from greater emphasis on coping strategies, craving psychoeducation, or adjunctive pharmacotherapy [83][84][85] .…”

Section: Discussionmentioning

confidence: 99%

Craving mediates the effect of impulsivity on lapse-risk during alcohol use disorder treatment

Coates

Gullo

Feeney

et al. 2020

Addictive Behaviors

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Background: Rash impulsiveness, the propensity for approach behaviour despite potential negative consequences, is associated with stronger alcohol craving in patients with Alcohol Use Disorder (AUD). This relationship is poorly understood and implications for treatment response unexamined. This study explored the relationship between rash impulsiveness, craving, and treatment response in a sample of AUD outpatients.Design: Longitudinal study conducted over a 12-week intervention period.Setting: University public hospital outpatient alcohol and drug clinic. Participants: Patients attending an abstinence-based Cognitive-Behavioural Therapy program for AUD (N = 304).Procedure: Assessments were completed pre-and-post treatment. Craving and alcohol consumption were assessed at each treatment session.Results: Higher rash impulsiveness predicted more frequent craving over treatment (b = 0.95, 95% CI = 0.40, 1.50). Higher craving was associated with greater lapse-risk (b = 0.04, 95% CI = 0.03, 0.05). The association between craving and lapse-risk increased as treatment progressed (b = 0.01, 95% CI = 0.01, 0.02). Craving positively mediated the relationship between impulsivity and lapse-risk (µ = 0.38, 95% CI = 0.10, 0.70). Conclusions:Craving mediates the effect of impulsivity in the prediction of lapse during abstinence-oriented treatment for AUD. Frequent assessment and management of craving during treatment is recommended to reduce alcohol lapse.

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“…In adaptive interventions (AIs), different intervention options (e.g., different types, intensities or modalities of delivery of treatment) are offered based on ongoing information about the individual's changing conditions. An AI is not an experimental design; it is an intervention design (i.e., the approach and specifics of an intervention program; see Nahum-Shani et al, 2017) that seeks to address the unique and changing needs of individuals as they progress over time through an intervention program. An AI (also known as a dynamic treatment regime in the statistical literature; see, e.g., Murphy, 2003;Chakraborty & Moodie, 2013;Kosorok & Moodie, 2016) is a protocol that guides for whom and under what conditions different intervention options should be offered, typically operationalized using a sequence of decision rules involving some tailoring variables.…”

Section: Introductionmentioning

confidence: 99%

Noninferiority and equivalence tests in sequential, multiple assignment, randomized trials (SMARTs).

Ghosh

Nahum‐Shani²,

Spring

et al. 2020

Psychological Methods

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Adaptive interventions (AIs) are increasingly becoming popular in behavioral sciences. An AI is a sequence of decision rules that specify for whom and under what conditions different intervention options should be offered, in order to address the changing needs of individuals as they progress over time. The sequential, multiple assignment, randomized trial (SMART) is a novel trial design that was developed to aid in empirically constructing effective AIs. The sequential randomizations in a SMART often yield multiple AIs that are embedded in the trial by design. Many SMARTs are motivated by scientific questions pertaining to the comparison of such embedded AIs. Existing data analytic methods and sample size planning resources for SMARTs are suitable only for superiority testing, namely for testing whether one embedded AI yields better primary outcomes on average than another. This calls for non-inferiority/equivalence testing methods, since AIs are often motivated by the need to deliver support/care in a less costly or less burdensome manner, while still yielding benefits that are equivalent or non-inferior to those produced by a more costly/burdensome standard of care. Here, we develop data analytic methods and sample size formulas for SMART studies aiming to test the non-inferiority or equivalence of one AI over another. Sample size and power considerations are discussed with supporting simulations, and online sample size planning resources are provided. A simulated data analysis shows how to test a non-inferiority or equivalence hypothesis with SMART data. For illustration, we use an example from a SMART study aiming to develop an AI for promoting weight loss among overweight/obese adults.

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A SMART data analysis method for constructing adaptive treatment strategies for substance use disorders

Cited by 42 publications

References 45 publications

Developing Optimized Adaptive Interventions in Education

Developing Optimized Adaptive Interventions in Education

Craving mediates the effect of impulsivity on lapse-risk during alcohol use disorder treatment

Noninferiority and equivalence tests in sequential, multiple assignment, randomized trials (SMARTs).

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