What to do after smoking relapse? A sequential multiple assignment randomized trial of chronic care smoking treatments.

AIM: To compare effects of three post-relapse interventions on smoking abstinence. DESIGN: Sequential three-phase multiple assignment randomized trial (SMART). SETTING: Eighteen Wisconsin, USA, primary care clinics. PARTICIPANTS: A total of 1154 primary care patients (53.6% women, 81.2% White) interested in quitting smoking enrolled from 2015 to 2019; 582 relapsed and were randomized to relapse recovery treatment. INTERVENTIONS: In phase 1, patients received cessation counseling and 8 weeks nicotine patch. Those who relapsed and agreed were randomized to a phase 2 relapse recovery group: (1) reduction counseling + nicotine mini-lozenges + encouragement to quit starting 1 month post-randomization (preparation); (2) repeated encouragement to quit starting immediately post-randomization (recycling); or (3) advice to call the tobacco quitline (control). The first two groups could opt into phase 3 new quit treatment [8 weeks nicotine patch + mini-lozenges plus randomization to two treatment factors (skill training and supportive counseling) in a 2 × 2 design]. Phase 2 and 3 interventions lasted ≤ 15 months. MEASUREMENTS: The study was powered to compare each active phase 2 treatment with the control on the primary outcome: biochemically confirmed 7-day point-prevalence abstinence 14 months post initiating phase 2 relapse recovery treatment. Exploratory analyses tested for phase 3 counseling factor effects. FINDINGS: Neither skill training nor supportive counseling (each on versus off) increased 14-month abstinence rates; skills on versus off 9.3% (14/151) versus 5.2% (8/153), P = 0.19; support on versus off 6.6% (10/152) versus 7.9% (12/152), P = 0.73. Phase 2 preparation did not produce higher 14-month abstinence rates than quitline referral; 3.6% (8/220) versus 2.1% [3/145; risk difference = 1.5%, 95% confidence interval (CI) = -1.8-5.0%, odds ratio (OR) = 1.8, 95% CI = 0.5-6.9]. Recycling, however, produced higher abstinence rates than quitline referral; 6.9% (15/217) versus 2.1% (three of 145; risk difference, 4.8%, 95% CI = 0.7-8.9%, OR = 3.5, 95% CI = 1.0-12.4). Recycling produced greater entry into new quit treatment than preparation: 83.4% (181/217) versus 55.9% (123/220), P < 0.0001. CONCLUSIONS: Among people interested in quitting smoking, immediate encouragement post-relapse to enter a new round of smoking cessation treatment ('recycling') produced higher probability of abstinence than tobacco quitline referral. Recycling produced higher rates of cessation treatment re-engagement than did preparation/cutting down using more intensive counseling and pharmacotherapy.

SMART Binary: New Sample Size Planning Resources for SMART Studies with Binary Outcome Measurements.

Sequential Multiple-Assignment Randomized Trials (SMARTs) play an increasingly important role in psychological and behavioral health research. This experimental approach enables researchers to answer scientific questions about how to sequence and match interventions to the unique, changing needs of individuals. A variety of sample size planning resources for SMART studies have been developed, enabling researchers to plan SMARTs for addressing different types of scientific questions. However, relatively limited attention has been given to planning SMARTs with binary (dichotomous) outcomes, which often require higher sample sizes relative to continuous outcomes. Existing resources for estimating sample size requirements for SMARTs with binary outcomes do not consider the potential to improve power by including a baseline measurement and/or multiple repeated outcome measurements. The current paper addresses this issue by providing sample size planning simulation procedures and approximate formulas for two-wave repeated measures binary outcomes (i.e., two measurement times for the outcome variable, before and after intervention delivery). The simulation results agree well with the formulas. We also discuss how to use simulations to calculate power for studies with more than two outcome measurement occasions. Results show that having at least one repeated measurement of the outcome can substantially improve power under certain conditions.

Adaptive Designs in Implementation Science and Practice: Their Promise and the Need for Greater Understanding and Improved Communication.

The promise of adaptation and adaptive designs in implementation science has been hindered by the lack of clarity and precision in defining what it means to adapt, especially regarding the distinction between adaptive study designs and adaptive implementation strategies. To ensure a common language for science and practice, authors reviewed the implementation science literature and found that the term adaptive was used to describe interventions, implementation strategies, and trial designs. To provide clarity and offer recommendations for reporting and strengthening study design, we propose a taxonomy that describes fixed versus adaptive implementation strategies and implementation trial designs. To improve impact, (a) future implementation studies should prespecify implementation strategy core functions that in turn can be taught to and replicated by health system/community partners, (b) funders should support exploratory studies that refine and specify implementation strategies, and (c) investigators should systematically address design requirements and ethical considerations (e.g., randomization, blinding/masking) with health system/community partners. Expected final online publication date for the Annual Review of Public Health, Volume 45 is April 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Optimizing Longitudinal Tobacco Cessation Treatment in Lung Cancer Screening: A Sequential, Multiple Assignment, Randomized Trial.

Importance: Nearly half of the 14.8 million US adults eligible for lung cancer screening (LCS) smoke cigarettes. The optimal smoking cessation program components for the LCS setting are unclear. Objective: To assess the effect of adding a referral to prescription medication therapy management (MTM) to the tobacco longitudinal care (TLC) program among patients eligible for LCS who smoke and do not respond to early tobacco treatment and to assess the effect of decreasing the intensity of TLC among participants who do respond to early treatment. Design, Setting, and Participants: This randomized clinical trial included patients who currently smoked cigarettes daily and were eligible for LCS. Recruitment took place at primary care centers and LCS programs at 3 large health systems in the US and began in October 2016, and 18-month follow-up was completed April 2021. Interventions: (1) TLC comprising intensive telephone coaching and combination nicotine replacement therapy for 1 year with at least monthly contact; (2) TLC with MTM, MTM offered pharmacist-referral for prescription medications; and (3) Quarterly TLC, intensity of TLC was decreased to quarterly contact. Intervention assignments were based on early response to tobacco treatment (abstinence) that was assessed either 4 weeks or 8 weeks after treatment initiation. Main outcomes and Measures: Self-reported, 6-month prolonged abstinence at 18-month. Results: Of 636 participants, 228 (35.9%) were female, 564 (89.4%) were White individuals, and the median (IQR) age was 64.3 (59.6-68.8) years. Four weeks or 8 weeks after treatment initiation, 510 participants (80.2%) continued to smoke (ie, early treatment nonresponders) and 126 participants (19.8%) had quit (ie, early treatment responders). The 18 month follow-up survey response rate was 83.2% (529 of 636). Across TLC groups at 18 months follow-up, the overall 6-month prolonged abstinence rate was 24.4% (129 of 529). Among the 416 early treatment nonresponders, 6-month prolonged abstinence for TLC with MTM vs TLC was 17.8% vs 16.4% (adjusted odds ratio [aOR] 1.13; 95% CI, 0.67-1.89). In TLC with MTM, 98 of 254 participants (39%) completed at least 1 MTM visit. Among 113 early treatment responders, 6-month prolonged abstinence for Quarterly TLC vs TLC was 24 of 55 (43.6%) vs 34 of 58 (58.6%) (aOR, 0.54; 95% CI, 0.25-1.17). Conclusions and Relevance: In this randomized clinical trial, adding referral to MTM with TLC for participants who did not respond to early treatment did not improve smoking abstinence. Stepping down to Quarterly TLC among early treatment responders is not recommended. Integrating longitudinal tobacco cessation care with LCS is feasible and associated with clinically meaningful quit rates. Trial Registration: ClinicalTrials.gov Identifier: NCT02597491.

Sequential, Multiple Assignment, Randomized Trial Designs.

This JAMA Guide to Statistics and Methods explains sequential, multiple assignment, randomized trial (SMART) study designs, in which some or all participants are randomized at 2 or more decision points depending on the participant's response to prior treatment.

Self-relevant appeals to engage in self-monitoring of alcohol use: A microrandomized trial.

OBJECTIVE: While self-monitoring can help mitigate alcohol misuse in young adults, engagement with digital self-monitoring is suboptimal. The present study investigates the utility of two types of digital prompts (reminders) to encourage young adults to self-monitor their alcohol use. These prompts leverage information that is self-relevant (i.e., represents and is valuable) to the person. METHOD: Five hundred ninety-one college students (Mage = 18; 61% = female, 76% = White) were enrolled in an 8-week intervention study involving biweekly digital self-monitoring of their alcohol use. At baseline, participants selected an item they would like to purchase for themselves and their preferred charitable organization. Then, biweekly, participants were microrandomized to a prompt highlighting the opportunity to either (a) win their preferred item (self-interest prompt); or (b) donate to their preferred charity (prosocial prompt). Following self-monitoring completion, participants allocated reward points toward lottery drawings for their preferred item or charity. RESULTS: The self-interest (vs. prosocial) prompt was significantly more effective in promoting proximal self-monitoring at the beginning of the study, Est = exp(.14) = 1.15; 95% confidence interval (CI) [1.01, 1.29], whereas the prosocial (vs. self-interest) prompt was significantly more effective at the end, Est = exp(-.17) = 0.84; 95% CI [0.70, 0.98]. Further, the prosocial (vs. self-interest) prompt was significantly more effective among participants who previously allocated all their reward points to drawings for their preferred item, Est = exp(-.15) = 0.86; 95% CI [.75, .97]. CONCLUSIONS: These results suggest that the advantage of prompts that appeal to a person's self-interest (vs. prosocial) motives varies over time and based on what reward options participants prioritized in previous decisions. Theoretical and practical implications for intervention design are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Estimating tree-based dynamic treatment regimes using observational data with restricted treatment sequences.

A dynamic treatment regime (DTR) is a sequence of decision rules that provide guidance on how to treat individuals based on their static and time-varying status. Existing observational data are often used to generate hypotheses about effective DTRs. A common challenge with observational data, however, is the need for analysts to consider "restrictions" on the treatment sequences. Such restrictions may be necessary for settings where (1) one or more treatment sequences that were offered to individuals when the data were collected are no longer considered viable in practice, (2) specific treatment sequences are no longer available, or (3) the scientific focus of the analysis concerns a specific type of treatment sequences (eg, "stepped-up" treatments). To address this challenge, we propose a restricted tree-based reinforcement learning (RT-RL) method that searches for an interpretable DTR with the maximum expected outcome, given a (set of) user-specified restriction(s), which specifies treatment options (at each stage) that ought not to be considered as part of the estimated tree-based DTR. In simulations, we evaluate the performance of RT-RL versus the standard approach of ignoring the partial data for individuals not following the (set of) restriction(s). The method is illustrated using an observational data set to estimate a two-stage stepped-up DTR for guiding the level of care placement for adolescents with substance use disorder.

Accounting for Context in Randomized Trials after Assignment.

Many preventive trials randomize individuals to intervention condition which is then delivered in a group setting. Other trials randomize higher levels, say organizations, and then use learning collaboratives comprised of multiple organizations to support improved implementation or sustainment. Other trials randomize or expand existing social networks and use key opinion leaders to deliver interventions through these networks. We use the term contextually driven to refer generally to such trials (traditionally referred to as clustering, where groups are formed either pre-randomization or post-randomization - i.e., a cluster-randomized trial), as these groupings or networks provide fixed or time-varying contexts that matter both theoretically and practically in the delivery of interventions. While such contextually driven trials can provide efficient and effective ways to deliver and evaluate prevention programs, they all require analytical procedures that take appropriate account of non-independence, something not always appreciated. Published analyses of many prevention trials have failed to take this into account. We discuss different types of contextually driven designs and then show that even small amounts of non-independence can inflate actual Type I error rates. This inflation leads to rejecting the null hypotheses too often, and erroneously leading us to conclude that there are significant differences between interventions when they do not exist. We describe a procedure to account for non-independence in the important case of a two-arm trial that randomizes units of individuals or organizations in both arms and then provides the active treatment in one arm through groups formed after assignment. We provide sample code in multiple programming languages to guide the analyst, distinguish diverse contextually driven designs, and summarize implications for multiple audiences.

Expanding access to medications for opioid use disorder in primary care clinics: an evaluation of common implementation strategies and outcomes.

BACKGROUND: To combat the opioid epidemic in the USA, unprecedented federal funding has been directed to states and territories to expand access to prevention, overdose rescue, and medications for opioid use disorder (MOUD). Similar to other states, California rapidly allocated these funds to increase reach and adoption of MOUD in safety-net, primary care settings such as Federally Qualified Health Centers. Typical of current real-world implementation endeavors, a package of four implementation strategies was offered to all clinics. The present study examines (i) the pre-post effect of the package of strategies, (ii) whether/how this effect differed between new (start-up) versus more established (scale-up) MOUD practices, and (iii) the effect of clinic engagement with each of the four implementation strategies. METHODS: Forty-one primary care clinics were offered access to four implementation strategies: (1) Enhanced Monitoring and Feedback, (2) Learning Collaboratives, (3) External Facilitation, and (4) Didactic Webinars. Using linear mixed effects models, RE-AIM guided outcomes of reach, adoption, and implementation quality were assessed at baseline and at 9 months follow-up. RESULTS: Of the 41 clinics, 25 (61%) were at MOUD start-up and 16 (39%) were at scale-up phases. Pre-post difference was observed for the primary outcome of percent of patient prescribed MOUD (reach) (ßtime = 3.99; 0.73 to 7.26; p = 0.02). The largest magnitude of change occurred in implementation quality (ES = 0.68; 95% CI = 0.66 to 0.70). Baseline MOUD capability moderated the change in reach (start-ups 22.60%, 95% CI = 16.05 to 29.15; scale-ups -4.63%, 95% CI = -7.87 to -1.38). Improvement in adoption and implementation quality were moderately associated with early prescriber engagement in Learning Collaboratives (adoption: ES = 0.61; 95% CI = 0.25 to 0.96; implementation quality: ES = 0.55; 95% CI = 0.41 to 0.69). Improvement in adoption was also associated with early prescriber engagement in Didactic Webinars (adoption: ES = 0.61; 95% CI = 0.20 to 1.05). CONCLUSIONS: Rather than providing an all-clinics-get-all-components package of implementation strategies, these data suggest that it may be more efficient and effective to tailor the provision of implementation strategies based on the needs of clinic. Future implementation endeavors could benefit from (i) greater precision in the provision of implementation strategies based on contextual determinants, and (ii) the inclusion of strategies targeting engagement.

Primary aim results of a clustered SMART for developing a school-level, adaptive implementation strategy to support CBT delivery at high schools in Michigan.

BACKGROUND: Schools increasingly provide mental health services to students, but often lack access to implementation strategies to support school-based (and school professional [SP]) delivery of evidence-based practices. Given substantial heterogeneity in implementation barriers across schools, development of adaptive implementation strategies that guide which implementation strategies to provide to which schools and when may be necessary to support scale-up. METHODS: A clustered, sequential, multiple-assignment randomized trial (SMART) of high schools across Michigan was used to inform the development of a school-level adaptive implementation strategy for supporting SP-delivered cognitive behavioral therapy (CBT). All schools were first provided with implementation support informed by Replicating Effective Programs (REP) and then were randomized to add in-person Coaching or not (phase 1). After 8 weeks, schools were assessed for response based on SP-reported frequency of CBT delivered to students and/or barriers reported. Responder schools continued with phase 1 implementation strategies. Slower-responder schools (not providing ≥ 3 CBT components to ≥10 students or >2 organizational barriers identified) were re-randomized to add Facilitation to current support or not (phase 2). The primary aim hypothesis was that SPs at schools receiving the REP + Coaching + Facilitation adaptive implementation strategy would deliver more CBT sessions than SPs at schools receiving REP alone. Secondary aims compared four implementation strategies (Coaching vs no Coaching × Facilitation vs no Facilitation) on CBT sessions delivered, including by type (group, brief and full individual). Analyses used a marginal, weighted least squares approach developed for clustered SMARTs. RESULTS: SPs (n = 169) at 94 high schools entered the study. N = 83 schools (88%) were slower-responders after phase 1. Contrary to the primary aim hypothesis, there was no evidence of a significant difference in CBT sessions delivered between REP + Coaching + Facilitation and REP alone (111.4 vs. 121.1 average total CBT sessions; p = 0.63). In secondary analyses, the adaptive strategy that offered REP + Facilitation resulted in the highest average CBT delivery (154.1 sessions) and the non-adaptive strategy offering REP + Coaching the lowest (94.5 sessions). CONCLUSIONS: The most effective strategy in terms of average SP-reported CBT delivery is the adaptive implementation strategy that (i) begins with REP, (ii) augments with Facilitation for slower-responder schools (schools where SPs identified organizational barriers or struggled to deliver CBT), and (iii) stays the course with REP for responder schools. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03541317 , May 30, 2018.

Obesity prevention practices in early care and education settings: an adaptive implementation trial.

BACKGROUND: Despite the potential for Early Care and Education (ECE) settings to promote healthy habits, a gap exists between current practices and evidence-based practices (EBPs) for obesity prevention in childhood. METHODS: We will use an enhanced non-responder trial design to determine the effectiveness and incremental cost-effectiveness of an adaptive implementation strategy for Together, We Inspire Smart Eating (WISE), while examining moderators and mediators of the strategy effect. WISE is a curriculum that aims to increase children's intake of carotenoid-rich fruits and vegetables through four evidence-based practices in the early care and education setting. In this trial, we will randomize sites that do not respond to low-intensity strategies to either (a) continue receiving low-intensity strategies or (b) receive high-intensity strategies. This design will determine the effect of an adaptive implementation strategy that adds high-intensity versus one that continues with low-intensity among non-responder sites. We will also apply explanatory, sequential mixed methods to provide a nuanced understanding of implementation mechanisms, contextual factors, and characteristics of sites that respond to differing intensities of implementation strategies. Finally, we will conduct a cost effectiveness analysis to estimate the incremental effect of augmenting implementation with high-intensity strategies compared to continuing low-intensity strategies on costs, fidelity, and child health outcomes. DISCUSSION: We expect our study to contribute to an evidence base for structuring implementation support in real-world ECE contexts, ultimately providing a guide for applying the adaptive implementation strategy in ECE for WISE scale-up. Our work will also provide data to guide implementation decisions of other interventions in ECE. Finally, we will provide the first estimate of relative value for different implementation strategies in this setting. TRIAL REGISTRATION: NCT05050539 ; 9/20/21.

Estimating time-varying causal excursion effect in mobile health with binary outcomes.

Advances in wearables and digital technology now make it possible to deliver behavioral mobile health interventions to individuals in their everyday life. The micro-randomized trial is increasingly used to provide data to inform the construction of these interventions. In a micro-randomized trial, each individual is repeatedly randomized among multiple intervention options, often hundreds or even thousands of times, over the course of the trial. This work is motivated by multiple micro-randomized trials that have been conducted or are currently in the field, in which the primary outcome is a longitudinal binary outcome. The primary aim of such micro-randomized trials is to examine whether a particular time-varying intervention has an effect on the longitudinal binary outcome, often marginally over all but a small subset of the individual's data. We propose the definition of causal excursion effect that can be used in such primary aim analysis for micro-randomized trials with binary outcomes. Under rather restrictive assumptions one can, based on existing literature, derive a semiparametric, locally efficient estimator of the causal effect. Starting from this estimator, we develop an estimator that can be used as the basis of a primary aim analysis under more plausible assumptions. Simulation studies are conducted to compare the estimators. We illustrate the developed methods using data from the micro-randomized trial, BariFit. In BariFit, the goal is to support weight maintenance for individuals who received bariatric surgery.

Main outcomes of M-bridge: A sequential multiple assignment randomized trial (SMART) for developing an adaptive preventive intervention for college drinking.

Objective: The goal was to develop a universal and resource-efficient adaptive preventive intervention (API) for incoming first-year students as a bridge to indicated interventions to address alcohol-related risks. The aims were to examine: (a) API versus assessment-only control, (b) the different APIs (i.e., 4 intervention sequences) embedded in the study design, and (c) moderators of intervention effects on binge drinking. Method: A sequential multiple assignment randomized trial (SMART) included two randomizations: timing (summer before vs. first semester) of universal personalized normative feedback and biweekly self-monitoring and, for heavy drinkers, bridging strategy (resource email vs. health coaching invitation). Participants (N = 891, 62.4% female, 76.8% White) were surveyed at the end of first and second semesters. The primary outcome was binge drinking frequency (4+/5+ drinks for females/males); secondary outcomes were alcohol consequences and health services utilization. Results: API (vs. control) was not significantly associated with outcomes. There were no differences between embedded APIs. Among heavy drinkers, the resource email (vs. health coach invitation) led to greater health services utilization. Moderator analyses suggested students intending to pledge into Greek life benefited more from any API (vs. control; 42% smaller increase from precollege in binge drinking frequency). Conclusions: Although overall effects were not significant, students at high risk (i.e., entering fraternities/sororities) did benefit more from the intervention. Furthermore, the resource email was effective for heavier drinkers. A technology-based strategy to deliver targeted resource-light interventions for heavy drinkers may be effective for reducing binge drinking during the transition to college. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Multitiered Systems of Support, Adaptive Interventions, and SMART Designs.

This article introduces the special section on adaptive interventions and sequential multiple-assignment randomized trial (SMART) research designs. In addition to describing the two accompanying articles, we discuss features of adaptive interventions (AIs) and describe the use of SMART design to optimize AIs in the context of multitiered systems of support (MTSS) and integrated MTSS. AI is a treatment delivery model that explicitly specifies how information about individuals should be used to decide which treatment to provide in practice. Principles that apply to the design of AIs may help to more clearly operationalize MTSS-based programs, improve their implementation in school settings, and increase their efficacy when used according to evidence-based decision rules. A SMART is a research design for developing and optimizing MTSS-based programs. We provide a running example of a SMART design to optimize an MTSS-aligned AI that integrates academic and behavioral interventions.

A sequential multiple assignment randomized trial (SMART) protocol for empirically developing an adaptive preventive intervention for college student drinking reduction.

College student alcohol use and associated negative consequences are clear public health problems with consequences including damage to self, others, and institutions. This paper describes the protocol of a research study designed to answer a number of important questions in the development of an adaptive preventive intervention (API) to reduce high-risk drinking among first-year college students. The API is designed to educate students and to motivate heavy-drinking college students to engage in existing resources to support reducing high-risk alcohol use, by leveraging technology-based intervention modalities. The primary outcome is a reduction in binge drinking, with secondary outcomes of reducing negative alcohol-related consequences and increasing health services utilization. Adaptive preventive interventions have the potential to reduce the acute and long-term negative health consequences of young adult alcohol use.

Identifying optimal level-of-care placement decisions for adolescent substance use treatment.

BACKGROUND: Adolescents respond differentially to substance use treatment based on their individual needs and goals. Providers may benefit from guidance (via decision rules) for personalizing aspects of treatment, such as level-of-care (LOC) placements, like choosing between outpatient or inpatient care. The field lacks an empirically-supported foundation to inform the development of an adaptive LOC-placement protocol. This work begins to build the evidence base for adaptive protocols by estimating them from a large observational dataset. METHODS: We estimated two-stage LOC-placement protocols adapted to individual adolescent characteristics collected from the Global Appraisal of Individual Needs assessment tool (n = 10,131 adolescents). We used a modified version of Q-learning, a regression-based method for estimating personalized treatment rules over time, to estimate four protocols, each targeting a potentially distinct treatment goal: one primary outcome (a composite of ten positive treatment outcomes) and three secondary (substance frequency, substance problems, and emotional problems). We compared the adaptive protocols to non-adaptive protocols using an independent dataset. RESULTS: Intensive outpatient was recommended for all adolescents at intake for the primary outcome, while low-risk adolescents were recommended for no further treatment at followup while higher-risk patients were recommended to inpatient. Our adaptive protocols outperformed static protocols by an average of 0.4 standard deviations (95 % confidence interval 0.2-0.6) of the primary outcome. CONCLUSIONS: Adaptive protocols provide a simple one-to-one guide between adolescents' needs and recommended treatment which can be used as decision support for clinicians making LOC-placement decisions.

The Balanced Opioid Initiative: protocol for a clustered, sequential, multiple-assignment randomized trial to construct an adaptive implementation strategy to improve guideline-concordant opioid prescribing in primary care.

BACKGROUND: Rates of opioid prescribing tripled in the USA between 1999 and 2015 and were associated with significant increases in opioid misuse and overdose death. Roughly half of all opioids are prescribed in primary care. Although clinical guidelines describe recommended opioid prescribing practices, implementing these guidelines in a way that balances safety and effectiveness vs. risk remains a challenge. The literature offers little help about which implementation strategies work best in different clinical settings or how strategies could be tailored to optimize their effectiveness in different contexts. Systems consultation consists of (1) educational/engagement meetings with audit and feedback reports, (2) practice facilitation, and (3) prescriber peer consulting. The study is designed to discover the most cost-effective sequence and combination of strategies for improving opioid prescribing practices in diverse primary care clinics. METHODS/DESIGN: The study is a hybrid type 3 clustered, sequential, multiple-assignment randomized trial (SMART) that randomizes clinics from two health systems at two points, months 3 and 9, of a 21-month intervention. Clinics are provided one of four sequences of implementation strategies: a condition consisting of educational/engagement meetings and audit and feedback alone (EM/AF), EM/AF plus practice facilitation (PF), EM/AF + prescriber peer consulting (PPC), and EM/AF + PF + PPC. The study's primary outcome is morphine-milligram equivalent (MME) dose by prescribing clinicians within clinics. The study's primary aim is the comparison of EM/AF + PF + PPC versus EM/AF alone on change in MME from month 3 to month 21. The secondary aim is to derive cost estimates for each of the four sequences and compare them. The exploratory aim is to examine four tailoring variables that can be used to construct an adaptive implementation strategy to meet the needs of different primary care clinics. DISCUSSION: Systems consultation is a practical blend of implementation strategies used in this case to improve opioid prescribing practices in primary care. The blend offers a range of strategies in sequences from minimally to substantially intensive. The results of this study promise to help us understand how to cost effectively improve the implementation of evidence-based practices. TRIAL REGISTRATION: NCT04044521 (ClinicalTrials.gov). Registered 05 August 2019.

SMART longitudinal analysis: A tutorial for using repeated outcome measures from SMART studies to compare adaptive interventions.

In recent years, there has been increased interest in the development of adaptive interventions across various domains of health and psychological research. An adaptive intervention is a protocolized sequence of individualized treatments that seeks to address the unique and changing needs of individuals as they progress through an intervention program. The sequential, multiple assignment, randomized trial (SMART) is an experimental study design that can be used to build the empirical basis for the construction of effective adaptive interventions. A SMART involves multiple stages of randomizations; each stage of randomization is designed to address scientific questions concerning the best intervention option to employ at that point in the intervention. Several adaptive interventions are embedded in a SMART by design; many SMARTs are motivated by scientific questions that concern the comparison of these embedded adaptive interventions. Until recently, analysis methods available for the comparison of adaptive interventions were limited to end-of-study outcomes. The current article provides an accessible and comprehensive tutorial to a new methodology for using repeated outcome data from SMART studies to compare adaptive interventions. We discuss how existing methods for comparing adaptive interventions in terms of end-of-study outcome data from a SMART can be extended for use with longitudinal outcome data. We also highlight the scientific utility of using longitudinal data from a SMART to compare adaptive interventions. A SMART study aiming to develop an adaptive intervention to engage alcohol- and cocaine-dependent individuals in treatment is used to demonstrate the application of this new methodology. (PsycINFO Database Record (c) 2020 APA, all rights reserved).