Multiple Assignment Randomized Trial Design Research Articles

Scaling and sustaining COVID-19 vaccination through meaningful community engagement and care coordination for underserved communities: hybrid type 3 effectiveness-implementation sequential multiple assignment randomized trial

BackgroundCOVID-19 inequities are abundant in low-income communities of color. Addressing COVID-19 vaccine hesitancy to promote equitable and sustained vaccination for underserved communities requires a multi-level, scalable, and sustainable approach. It is also essential that efforts acknowledge the broader healthcare needs of these communities including engagement in preventive services.MethodsThis is a hybrid type 3 effectiveness-implementation study that will include a multi-level, longitudinal, mixed-methods data collection approach designed to assess the sustained impact of a co-created multicomponent strategy relying on bidirectional learning, shared decision-making, and expertise by all team members. The study capitalizes on a combination of implementation strategies including mHealth outreach with culturally appropriate messaging, care coordination to increase engagement in high priority preventive services, and the co-design of these strategies using community advisory boards led by Community Weavers. Community Weavers are individuals with lived experience as members of an underserved community serving as cultural brokers between communities, public health systems, and researchers to co-create community-driven, culturally sensitive public health solutions. The study will use an adaptive implementation approach operationalized in a sequential multiple assignment randomized trial design of 300 participants from three sites in a Federally Qualified Health Center in Southern California. This design will allow examining the impact of various implementation strategy components and deliver more intensive support to those who benefit from it most. The primary effectiveness outcomes are COVID-19 vaccine completion, engagement in preventive services, and vaccine confidence. The primary implementation outcomes are reach, adoption, implementation, and maintenance of the multicomponent strategy over a 12-month follow-up period. Mixed-effects logistic regression models will be used to examine program impacts and will be triangulated with qualitative data from participants and implementers.DiscussionThis study capitalizes on community engagement, implementation science, health equity and communication, infectious disease, and public health perspectives to co-create a multicomponent strategy to promote the uptake of COVID-19 vaccination and preventive services for underserved communities in San Diego. The study design emphasizes broad engagement of our community and clinic partners leading to culturally sensitive and acceptable strategies to produce lasting and sustainable increases in vaccine equity and preventive services engagement.Trial registrationClinicalTrials.gov Identifier: NCT05841810 May 3, 2023

Use of a sequential multiple assignment randomized trial to test contingency management and an integrated behavioral economic and mindfulness intervention for buprenorphine-naloxone medication adherence for opioid use disorder

BackgroundBuprenorphine-naloxone is a medication shown to improve outcomes for individuals seeking treatment for opioid use disorder (OUD); however, outcomes are limited by low medication adherence rates. This is especially true during the early stages of treatment.MethodsThe present study proposes to utilize a sequential multiple assignment randomized trial design to compare two psychological interventions targeting buprenorphine-naloxone adherence: (1) contingency management (CM) and (2) brief motivational interviewing plus substance-free activities session plus mindfulness (BSM). Participants will be N = 280 adults who present to a university-based addictions clinic seeking treatment for OUD. Participants will be randomized to condition to receive 4 sessions of their assigned intervention (CM or BSM). Participants who are adherent, defined as attending physician appointments and having buprenorphine present in urine toxicology, will enter maintenance intervention for an additional 6 months. Those who are not adherent will be re-randomized to receive either the other intervention or both interventions. Follow-up will occur at 8 months post-randomization.ConclusionsThis novel design will examine the benefit of sequential treatment decisions following non-adherence. The primary outcome of this study is buprenorphine-naloxone medication adherence, as assessed by physician visit attendance and presence of buprenorphine in urine. Results will elicit the relative efficacy of CM and BSM compared to one another and whether keeping the initial treatment approach when adding the alternative approach for initially non-adherent individuals is beneficial.Trial registrationClinicalTrials.gov NCT04080180

Optimal allocation to treatments in a sequential multiple assignment randomized trial.

One of the main questions in the design of a trial is how many subjects should be assigned to each treatment condition. Previous research has shown that equal randomization is not necessarily the best choice. We study the optimal allocation for a novel trial design, the sequential multiple assignment randomized trial, where subjects receive a sequence of treatments across various stages. A subject's randomization probabilities to treatments in the next stage depend on whether he or she responded to treatment in the current stage. We consider a prototypical sequential multiple assignment randomized trial design with two stages. Within such a design, many pairwise comparisons of treatment sequences can be made, and a multiple-objective optimal design strategy is proposed to consider all such comparisons simultaneously. The optimal design is sought under either a fixed total sample size or a fixed budget. A Shiny App is made available to find the optimal allocations and to evaluate the efficiency of competing designs. As the optimal design depends on the response rates to first-stage treatments, maximin optimal design methodology is used to find robust optimal designs. The proposed methodology is illustrated using a sequential multiple assignment randomized trial example on weight loss management.

Evaluating Misclassification Effects on Single Sequential Treatment in Sequential Multiple Assignment Randomized Trial (SMART) Designs

Sequential multiple assignment randomized trial designs tailor individual treatment by rerandomizing participants to subsequent therapies based on their response to initial treatment. Misclassification of participant responses to initial treatment can lead to inappropriate treatment assignment and thus impact the final outcome. The aim of this study is to derive a series of formulas for quantifying potential misclassification effects on the mean, variance, and statistical inference of a single sequential treatment (SST) effect with continuous outcome. Relative bias is expressed as a function of sensitivity, specificity, and the probability of being true responders. Results show that misclassification can introduce bias to the estimated treatment effect. Though the magnitude of bias varies, there are a few general conclusions: (1) for any fixed sensitivity (or specificity) the relative bias of the mean of responders (or nonresponders) always approaches 0 in a monotonic nonlinear pattern as specificity (or sensitivity) increases; (2) the relative bias of SST variance always has nonmonotone nonlinear relationship with sensitivity or specificity; (3) the SST variance under misclassification is always over-estimated. Furthermore, the results show that misclassification can affect statistical inference, with power exhibiting either monotonic or nonmonotonic patterns and resulting in either under- or over-estimation.

An efficacy trial of adaptive interventions for alcohol use disorder

BackgroundAdaptive interventions, sometimes referred to as “stepped care”, are those interventions in which the type or dosage of treatment offered to patients is tailored to baseline clinical presentation and then adjusted over time in response to patient progress or lack thereof. Currently, no adaptive brief interventions exist specifically for alcohol use disorder (AUD). MethodThis study used a sequential multiple assignment randomized trial design with 160 individuals with AUD recruited both locally and nationally who had a goal to reduce or abstain from drinking. Participants received brief advice (BA) and then the study reassessed them three weeks later; the study randomized those who did not respond to BA, defined as reducing their drinking to low-risk guidelines, to two session of motivational interviewing (MI) or more BA. The study then reassessed participants at week 8. The study re-randomized nonresponders to receive either MI alone or MI plus behavioral self-control therapy (BSCT), also referred to as coping skills therapy, and evaluated participants at week 13. ResultsOverall, participants receiving any BSCT made the greatest reductions in drinking. Participants who received MI at week 4 and BSCT at week 8 outperformed all other groups. ConclusionFindings reveal that prolonged treatment, more sessions, and/or a specific combination of MI and BSCT provided optimal outcomes. Future research should determine whether such an algorithm holds across heterogenous groups of individuals with AUD.

Evaluation of a Stepped-Care eHealth HIV Prevention Program for Diverse Adolescent Men Who Have Sex With Men: Protocol for a Hybrid Type 1 Effectiveness Implementation Trial of SMART.

BackgroundAdolescent men who have sex with men (AMSM), aged 13 to 18 years, account for more than 80% of teen HIV occurrences. Despite this disproportionate burden, there is a conspicuous lack of evidence-based HIV prevention programs. Implementation issues are critical as traditional HIV prevention delivery channels (eg, community-based organizations, schools) have significant access limitations for AMSM. As such, eHealth interventions, such as our proposed SMART program, represent an excellent modality for delivering AMSM-specific intervention material where youth are.ObjectiveThis randomized trial aimed to test the effectiveness of the SMART program in reducing condom-less anal sex and increasing condom self-efficacy, condom use intentions, and HIV testing for AMSM. We also plan to test whether SMART has differential effectiveness across important subgroups of AMSM based on race and ethnicity, urban versus rural residence, age, socioeconomic status, and participation in an English versus a Spanish version of SMART.MethodsUsing a sequential multiple assignment randomized trial design, we will evaluate the impact of a stepped-care package of increasingly intensive eHealth interventions (ie, the universal, information-based SMART Sex Ed; the more intensive, selective SMART Squad; and a higher cost, indicated SMART Sessions). All intervention content is available in English and Spanish. Participants are recruited primarily from social media sources using paid and unpaid advertisements.ResultsThe trial has enrolled 1285 AMSM aged 13 to 18 years, with a target enrollment of 1878. Recruitment concluded in June 2020. Participants were recruited from 49 US states as well as Puerto Rico and the District of Columbia. Assessments of intervention outcomes at 3, 6, 9, and 12 months are ongoing.ConclusionsSMART is the first web-based program for AMSM to take a stepped-care approach to sexual education and HIV prevention. This design indicates that SMART delivers resources to all adolescents, but more costly treatments (eg, video chat counseling in SMART Sessions) are conserved for individuals who need them the most. SMART has the potential to reach AMSM to provide them with a sex-positive curriculum that empowers them with the information, motivation, and skills to make better health choices.Trial RegistrationClinicalTrials.gov Identifier NCT03511131; https://clinicaltrials.gov/ct2/show/NCT03511131International Registered Report Identifier (IRRID)DERR1-10.2196/19701

Comparing adaptive interventions under a general sequential multiple assignment randomized trial design via multiple comparisons with the best

This paper considers screening of adaptive interventions or adaptive treatment strategies embedded in a sequential multiple assignment randomized trial (SMART). As a SMART typically consists of numerous adaptive interventions, inferential procedures based on pairwise comparisons of all interventions may suffer substantial loss in efficiency after accounting for multiplicity. We propose simultaneous confidence intervals that compare the values of interventions of interest to that of the unknown best intervention by generalizing the method in Edwards and Hsu (1983). The multiple comparison with the best (MCB) intervals are applied as screening tool: an intervention with MCB interval excluding zero will be declared as inferior to the true best at a pre-specified confidence level, and hence excluded from further exploration. Simulation studies show that the proposed method outperforms the multiple comparison procedures based on Bonferroni’s correction in terms of width of confidence intervals for estimation. The method is applied to analyze data from the CODIACS trial in patients with depression.

A small n sequential multiple assignment randomized trial design for use in rare disease research

BackgroundClinical trials in rare diseases are difficult to conduct due to the limited number of patients available with each disorder. We developed a Phase 2 trial which is a small n sequential multiple assignment randomized trial (snSMART) design to test several treatments for a rare disease for which no standard therapy exists. PurposeThis paper illustrates the design, sample size estimation and operating characteristics of an snSMART. MethodsWe investigate the performance of a class of weighted Z statistics via computer simulations. ResultsWe demonstrate the increase in power over traditional single stage designs, and indicate how the power changes as a function of the weight given to each stage. ConclusionThe snSMART design is promising in a rare disease setting where several alternative treatments are under consideration and small sample sizes are necessary.

Dynamic Treatment Regimes.

A dynamic treatment regime consists of a sequence of decision rules, one per stage of intervention, that dictate how to individualize treatments to patients based on evolving treatment and covariate history. These regimes are particularly useful for managing chronic disorders, and fit well into the larger paradigm of personalized medicine. They provide one way to operationalize a clinical decision support system. Statistics plays a key role in the construction of evidence-based dynamic treatment regimes - informing best study design as well as efficient estimation and valid inference. Due to the many novel methodological challenges it offers, this area has been growing in popularity among statisticians in recent years. In this article, we review the key developments in this exciting field of research. In particular, we discuss the sequential multiple assignment randomized trial designs, estimation techniques like Q-learning and marginal structural models, and several inference techniques designed to address the associated non-standard asymptotics. We reference software, whenever available. We also outline some important future directions.