Sequential Multiple Assignment Randomized Trial Design Research Articles

Randomized Trial of the Effectiveness of Videoconferencing-Based Versus Message-Based Psychotherapy on Depression.

The authors compared the engagement, clinical outcomes, and adverse events of text or voice message-based psychotherapy (MBP) versus videoconferencing-based psychotherapy (VCP) among adults with depression. The study used a sequential multiple-assignment randomized trial design with data drawn from phase 1 of a two-phase small business innovation research study. In total, 215 adults (ages ≥18 years) with depression received care from Talkspace, a digital mental health care company. Participants were initially randomly assigned to receive either asynchronous MBP or weekly VCP. All therapists provided evidence-based treatments such as cognitive-behavioral therapy. After 6 weeks of treatment, participants whose condition did not show a response on the Patient Health Questionnaire-9 or was rated as having not improved on the Clinical Global Impressions scale were randomly reassigned to receive either weekly VCP plus MBP or monthly VCP plus MBP. Longitudinal mixed-effects models with piecewise linear time trends applied to multiple imputed data sets were used to address missingness of data. Participants who were initially assigned to the MBP condition engaged with their therapists over more weeks than did participants in the VCP condition (7.8 weeks for MBP vs. 4.9 weeks for VCP; p<0.001). No meaningful differences were observed between the two groups in rates of change by 6 or 12 weeks for depression, anxiety, disability, or global ratings of improvement. Neither treatment resulted in any adverse events. MBP appears to be a viable alternative to VCP for treating adults with depression.

Incorporating participants' welfare into sequential multiple assignment randomized trials.

Dynamic treatment regimes (DTRs) are sequences of decision rules that recommend treatments based on patients' time-varying clinical conditions. The sequential, multiple assignment, randomized trial (SMART) is an experimental design that can provide high-quality evidence for constructing optimal DTRs. In a conventional SMART, participants are randomized to available treatments at multiple stages with balanced randomization probabilities. Despite its relative simplicity of implementation and desirable performance in comparing embedded DTRs, the conventional SMART faces inevitable ethical issues, including assigning many participants to the empirically inferior treatment or the treatment they dislike, which might slow down the recruitment procedure and lead to higher attrition rates, ultimately leading to poor internal and external validities of the trial results. In this context, we propose a SMART under the Experiment-as-Market framework (SMART-EXAM), a novel SMART design that holds the potential to improve participants' welfare by incorporating their preferences and predicted treatment effects into the randomization procedure. We describe the steps of conducting a SMART-EXAM and evaluate its performance compared to the conventional SMART. The results indicate that the SMART-EXAM can improve the welfare of the participants enrolled in the trial, while also achieving a desirable ability to construct an optimal DTR when the experimental parameters are suitably specified. We finally illustrate the practical potential of the SMART-EXAM design using data from a SMART for children with attention-deficit/hyperactivity disorder.

Contingency management plus acceptance and commitment therapy for initial cocaine abstinence: Results of a sequential multiple assignment randomized trial (SMART)

BackgroundThis study tested an adaptive intervention for optimizing abstinence outcomes over phases of treatment for cocaine use disorder using a SMART design. Phase 1 assessed whether 4 weeks of contingency management (CM) improved response with the addition of Acceptance and Commitment Therapy (ACT). Phase 2 assessed pharmacological augmentation with modafinil (MOD) vs. placebo (PLA) for individuals not achieving abstinence during Phase 1. MethodFor Phase 1 of treatment, participants (N=118) were randomly allocated to ACT+CM or Drug Counseling (DC+CM), the comparison condition. At week 4, treatment response was defined as the submission of six consecutive cocaine-negative urine drug screens (UDS). Phase 1 non-responders were re-randomized to MOD or PLA as adjunct to their initial treatment. Phase 1 responders continued receiving their initial treatment. Primary outcomes included response rate and proportion of cocaine-negative UDS for Phase 1 and 2. Analyses used Bayesian inference with 80% pre-specified as the posterior probability (PP) threshold constituting moderate evidence that an effect exists. ResultsPhase 1 response was higher in the ACT+CM group (24.5%) compared to the DC+CM group (17.5%; PP = 84.5%). In Phase 2, the proportion of cocaine-negative UDS among Phase 1 responders did not differ by initial treatment (PP = 61.8%) but remained higher overall compared to Phase 1 non-responders (PPs > 99%). No evidence of an effect favoring augmentation with MOD was observed. DiscussionAdding ACT to CM increased abstinence initiation. Initial responders were more likely to remain abstinent compared to initial non-responders, for whom modafinil was not an effective pharmacotherapy augmentation strategy.

Social Media and Online Dating Safety Practices by Adolescent Sexual and Gender Diverse Men: Mixed-Methods Findings From the SMART Study

PurposeSexual and gender diverse youth (SGDY) develop and employ safety strategies on their own to manage risks while using dating apps. This study aimed to describe the online dating safety practices of SGDY and determine the effectiveness of an eHealth HIV-prevention educational intervention with dating safety content to promote future safety behaviors. MethodsSGDY aged 13–18 from across the United States (N = 1087) were assigned to increasingly intensive HIV-prevention educational programs using a sequential multiple-assignment randomized trial design. Data were collected at three time points, each three months apart. Participants were asked multiple–choice and open-ended questions about the safety practices they used online and in person; mixed-methods described the safety behaviors of SGDY. Logistic regressions were used to determine psychosocial predictors of safety behaviors and the effectiveness of the intervention in promoting future safety practices. Results60% (n = 662) of participants used dating apps, most of whom reported using online (96.4%, n = 638) and in-person (92.9%, n = 615) safety strategies, such as limiting the disclosure of personal information or meeting other users in public. Outness and previous victimization were important psychosocial predictors of engaging in safety behaviors. Additionally, participants who received the online safety education were over 50% more likely to employ certain safety behaviors than SGDY who did not. DiscussionWhile most SGDY reported at least 1 type of safety strategy when dating online, safety practices differed across psychosocial variables, such as outness. This study provides evidence for the effectiveness of an eHealth educational intervention tailored to SGDY to promote additional safety behaviors.

Re-conceptualizing SMART designs as a hybrid of randomized and regression discontinuity designs: opportunities, cautions

ABSTRACT Though previously unacknowledged, a SMART (Sequential Multiple Assignment Randomized Trial) design uses both regression discontinuity (RD) and randomized controlled trial (RCT) designs. This combination structure creates a conceptual symbiosis between the two designs that enables both RCT- and previously unrecognized, RD-based inferential claims. In Phase 1, based on a quantitative cut-point, a SMART design yields successful or unsuccessful participants after the initial RCT. In Phase 2, interventions are randomly assigned to successful and unsuccessful, Phase 1 subgroups, thereby yielding both RCT and RD results. Like RCT-based inferences, many but not all newly recognized, RD-based SMART inferences are potentially unbiased. In particular, comparisons between RCT and RD outcomes are potentially problematic. Principles from the within-study comparison literature are used to inform SMART-based research, thereby introducing otherwise ignored inferential and procedural possibilities. Awareness of RD designs embedded within SMARTs encourages previously unrecognized inferential claims, though potential internal validity threats and limitations must be carefully weighed. Finally, new, SMART-like sequences of quasi-experiments and experiments can be tested and embedded logic applied.

Optimization of a new adaptive intervention using the SMART Design to increase COVID-19 testing among people at high risk in an urban community

BackgroundCOVID-19 has impacted the health and social fabric of individuals and families living across the USA, and it has disproportionately affected people living in urban communities with co-morbidities, those working in high-risk settings, refusing or unable to adhere to CDC guidelines, and more. Social determinants of health (SDH), such as stigmatization, incarceration, and poverty, have been associated with increased exposure to COVID-19 and increased deaths. While vaccines and booster shots are available, it will take time to reach herd immunity, and it is unclear how long newly developed vaccines provide protection and how effective they are against emerging variants. Therefore, prevention methods recommended by the Centers for Disease and Control (CDC)—i.e., testing, hand-washing, social distancing, contact tracing, vaccination and booster shots, and quarantine—are essential to reduce the rates of COVID-19 in marginalized communities. This project will adapt and test evidence-based HIV interventions along the prevention and treatment cascade to help address COVID-19 prevention needs.MethodsThe study aims to (1) optimize an adaptive intervention that will increase rates of testing and adherence to New Jersey State COVID-19 recommendations (testing, social distancing, quarantine, hospitalization, contact tracing, and acceptance of COVID-19 vaccination and booster shots) among high-risk populations and (2) identify predictors of testing completion and adherence to New Jersey recommendations. This study follows Community Based Participatory Research (CBPR) principles to conduct a Sequential, Multiple Assignment Randomized Trial (SMART) with 670 COVID-19 medically/socially vulnerable people. Participants will be recruited using a variety of strategies including advertisements on social media, posting fliers in public places, street outreach, facility-based, and snowball sampling. Participants complete a baseline survey and are randomized to receive navigation services or an electronic brochure. They then complete a follow-up 7 days after baseline and are randomized again to either continue with their original assignment or switch to the other intervention or critical dialog or brief counseling. Participants then complete a 5-week post-baseline follow-up. Guided by the COVID-19 Continuum of Prevention, Care, and Treatment, the analysis will explore the factors associated with COVID-19 testing within 7 days of the intervention.DiscussionThis paper describes the protocol of the first study to use SMART following CBPR to adapt evidence-based HIV prevention interventions to COVID-19. The findings will inform the development of an effective and scalable adaptive intervention to increase COVID-19 testing and adherence to public health recommendations, including vaccination and booster shots, among a marginalized and difficult-to-engage population.Trial registrationClinicalTrials.govNCT04757298. Registered on February 17, 2021.

A technology-based intervention to improve safety, mental health and empowerment outcomes for immigrant women with intimate partner violence experiences: it\u2019s weWomen plus sequential multiple assignment randomized trial (SMART) protocol

BackgroundIntimate partner violence (IPV) disproportionately affects immigrant women, an understudied and underserved population in need for evidence-based rigorously evaluated culturally competent interventions that can effectively address their health and safety needs.MethodsThis study uses a sequential, multiple assignment, randomized trial (SMART) design to rigorously evaluate an adaptive, trauma-informed, culturally tailored technology-delivered intervention tailored to the needs of immigrant women who have experienced IPV. In the first stage randomization, participants are randomly assigned to an online safety decision and planning or a usual care control arm and safety, mental health and empowerment outcomes are assessed at 3-, 6- and 12-months post-baseline. For the second stage randomization, women who do not report significant improvements in safety (i.e., reduction in IPV) and empowerment from baseline to 3 months follow up (i.e., non-responders) are re- randomized to safety and empowerment strategies delivered via text only or a combination of text and phone calls with trained advocates. Data on outcomes (safety, mental health, and empowerment) for early non-responders is assessed at 6 and 12 months post re-randomization.DiscussionThe study’s SMART design provides an opportunity to implement and evaluate an individualized intervention protocol for immigrant women based on their response to type or intensity of intervention. The findings will be useful for identifying what works for whom and characteristics of participants needing a particular type or intensity level of intervention for improved outcomes. If found to be effective, the study will result in an evidence-based trauma-informed culturally tailored technology-based safety decision and planning intervention for immigrant survivors of IPV that can be implemented by practitioners serving immigrant women in diverse settings.Trial registrationThis trial was registered with ClinicalTrials.gov as NCT04098276 on September 13, 2019.

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.

Sequential Multiple Assignment Randomized Trial (SMART) to identify optimal sequences of telemedicine interventions for improving initiation of insulin therapy: A simulation study

BackgroundTo examine the value of a Sequential Multiple Assignment Randomized Trial (SMART) design compared to a conventional randomized control trial (RCT) for telemedicine strategies to support titration of insulin therapy for Type 2 Diabetes Mellitus (T2DM) patients new to insulin.MethodsMicrosimulation models were created in R using a synthetic sample based on primary data from 63 subjects enrolled in a pilot study of a smartphone application (App), Diabetes Pal compared to a nurse-based telemedicine strategy (Nurse). For comparability, the SMART and an RCT design were constructed to allow comparison of four (embedded) adaptive interventions (AIs).ResultsIn the base case scenario, the SMART has similar overall mean expected HbA1c and cost per subject compared with RCT, for sample size of n = 100 over 10,000 simulations. SMART has lower (better) standard deviations of the mean expected HbA1c per AI, and higher efficiency of choosing the correct AI across various sample sizes. The differences between SMART and RCT become apparent as sample size decreases. For both trial designs, the threshold value at which a subject was deemed to have been responsive at an intermediate point in the trial had an optimal choice (i.e., the sensitivity curve had a U-shape). SMART design dominates the RCT, in the overall mean HbA1c (lower value) when the threshold value is close to optimal.ConclusionsSMART is suited to evaluating the efficacy of different sequences of treatment options, in addition to the advantage of providing information on optimal treatment sequences.

Deeply Tailoring Adaptive Interventions: Enhancing Knowledge Generation of SMARTs in Special Education

Special educators serve a diverse population of students with unique strengths and needs, and adaptive interventions that account for individual differences before and during the intervention are an important tool to moving the field toward more individualized practices. The purpose of this article is to detail the conceptualization and application of tailoring the sequential multiple assignment randomized trial (SMART) design approach to developing and evaluating deeply tailored adaptive interventions through the application of secondary analyses to account for individual differences at multiple time points. This conceptual paper provides an overview beyond the basic SMART design components by describing the tactics, design options, and analyses currently available to further refine a SMART study into a more personalized intervention to account for individual differences at multiple points throughout the intervention and individual response to treatment.

Use of a Smartphone App to Increase Physical Activity Levels in Insufficiently Active Adults: Feasibility Sequential Multiple Assignment Randomized Trial (SMART)

BackgroundThe sequential multiple assignment randomized trial (SMART) design allows for changes in the intervention during the trial period. Despite its potential and feasibility for defining the best sequence of interventions, so far, it has not been utilized in a smartphone/gamified intervention for physical activity.ObjectiveWe aimed to investigate the feasibility of the SMART design for assessing the effects of a smartphone app intervention to improve physical activity in adults. We also aimed to describe the participants’ perception regarding the protocol and the use of the app for physical activity qualitatively.MethodsWe conducted a feasibility 24-week/two-stage SMART in which 18 insufficiently active participants (<10,000 steps/day) were first randomized to group 1 (smartphone app only), group 2 (smartphone app + tailored messages), and a control group (usual routine during the protocol). Participants were motivated to increase their step count by at least 2000 steps/day each week. Based on the 12-week intermediate outcome, responders continued the intervention and nonresponders were rerandomized to subsequent treatment, including a new group 3 (smartphone app + tailored messages + gamification) in which they were instructed to form groups to use several game elements available in the chosen app (Pacer). We considered responders as those with any positive slope in the linear relationship between weeks and steps per day at the end of the first stage of the intervention. We compared the accelerometer-based steps per day before and after the intervention, as well as the slopes of the app-based steps per day between the first and second stages of the intervention.ResultsTwelve participants, including five controls, finished the intervention. We identified two responders in group 1. We did not observe relevant changes in the steps per day either throughout the intervention or compared with the control group. However, the rerandomization of five nonresponders led to a change in the slope of the steps per day (median −198 steps/day [IQR −279 to −103] to 20 steps/day [IQR −204 to 145]; P=.08). Finally, in three participants from group 2, we observed an increase in the number of steps per day up to the sixth week, followed by an inflection to baseline values or even lower (ie, a quadratic relationship). The qualitative analysis showed that participants’ reports could be classified into the following: (1) difficulty in managing the app and technology or problems with the device, (2) suitable response to the app, and (3) difficulties to achieve the goals.ConclusionsThe SMART design was feasible and changed the behavior of steps per day after rerandomization. Rerandomization should be implemented earlier to take advantage of tailored messages. Additionally, difficulties with technology and realistic and individualized goals should be considered in interventions for physical activity using smartphones.Trial RegistrationBrazilian Registry of Clinical Trials RBR-8xtc9c; http://www.ensaiosclinicos.gov.br/rg/RBR-8xtc9c/.

Protocol: examining the effectiveness of an adaptive implementation intervention to improve uptake of the VA suicide risk identification strategy: a sequential multiple assignment randomized trial

BackgroundIn 2018, the Veterans Health Administration (VHA) mandated implementation of a national suicide risk identification strategy (Risk ID). The goal of Risk ID is to improve the detection and management of suicide risk by standardizing suicide risk screening and evaluation enterprise-wide. In order to ensure continuous quality improvement (QI), ongoing evaluation and targeted interventions to improve implementation of Risk ID are needed. Moreover, given that facilities will vary with respect to implementation needs and barriers, the dose and type of intervention needed may vary across facilities. Thus, the objective of this study is to examine the effectiveness of an adaptive implementation strategy to improve the uptake of suicide risk screening and evaluation in VHA ambulatory care settings. In addition, this study will examine specific factors that may impact the uptake of suicide risk screening and evaluation and the adoption of different implementation strategies. This protocol describes the stepped implementation approach and proposed evaluation plan.MethodsUsing a sequential multiple assignment randomized trial (SMART) design, two evidence-based implementation strategies will be evaluated: (1) audit and feedback (A&F); (2) A&F plus external facilitation (A&F + EF). Implementation outcomes of interest include uptake of secondary suicide risk screening and uptake of comprehensive suicide risk evaluation (stages 2 and 3 of Risk ID). Secondary outcomes include rates of other clinical outcomes (i.e., safety planning) and organizational factors that may impact Risk ID implementation (i.e., leadership climate and leadership support).DiscussionThis national QI study will use a SMART design to evaluate whether an adaptive implementation strategy is effective in improving uptake of a mandated VHA-wide suicide risk screening and evaluation initiative. If this study finds that the proposed stepped implementation strategy is effective at increasing uptake and maintaining performance improvements, this approach may be used as an overarching QI strategy for other national suicide prevention programs.Trial registrationClinicalTrials.gov NCT04243330. Registered 28 January 2020

Study protocol for a sequential multiple assignment randomized trial (SMART) to improve physical activity in employed women

IntroductionPhysical activity monitors, motivational text messages, personal calls, and group meetings, have proven to be efficacious physical activity interventions. However, individual participant response to these interventions varies drastically. A SMART design (sequential multiple assignment randomized trial) provides an effective way to test interventions that start with an initial treatment and then transition to an augmented treatment for non-responders. We describe a SMART to determine the most effective adaptive intervention to increase physical activity (steps, moderate-to-vigorous physical activity) and improve cardiovascular health among employed women who are not regularly physically active. The SMART uses combinations of four treatments: 1) enhanced physical activity monitor (Fitbit wearable activity monitor and mobile app with goal setting and physical activity prescription), 2) text messages, 3) personal calls, and 4) group meetings. MethodsParticipants (N = 312) include women ages 18–70 employed at a large academic medical center. Women will be randomized to an initial intervention, either an enhanced physical activity monitor or enhanced physical activity monitor + text messaging. Non-responders to the initial intervention at 2 months will be randomized to either personal calls or groups meetings for the next 6 months. At 8 months, all participants will return to only an enhanced physical activity monitor until their final 12-month assessment. DiscussionResults of this study will add to the literature on improving physical activity in employed women. This study will identify effective interventions for women who respond to less intensive treatments, while maximizing benefits for those who need a more intensive approach.

Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome

Clinicians and researchers alike are increasingly interested in how best to personalize interventions. A dynamic treatment regimen is a sequence of prespecified decision rules which can be used to guide the delivery of a sequence of treatments or interventions that is tailored to the changing needs of the individual. The sequential multiple-assignment randomized trial is a research tool which allows for the construction of effective dynamic treatment regimens. We derive easy-to-use formulae for computing the total sample size for three common two-stage sequential multiple-assignment randomized trial designs in which the primary aim is to compare mean end-of-study outcomes for two embedded dynamic treatment regimens which recommend different first-stage treatments. The formulae are derived in the context of a regression model which leverages information from a longitudinal outcome collected over the entire study. We show that the sample size formula for a sequential multiple-assignment randomized trial can be written as the product of the sample size formula for a standard two-arm randomized trial, a deflation factor that accounts for the increased statistical efficiency resulting from a longitudinal analysis, and an inflation factor that accounts for the design of a sequential multiple-assignment randomized trial. The sequential multiple-assignment randomized trial design inflation factor is typically a function of the anticipated probability of response to first-stage treatment. We review modeling and estimation for dynamic treatment regimen effect analyses using a longitudinal outcome from a sequential multiple-assignment randomized trial, as well as the estimation of standard errors. We also present estimators for the covariance matrix for a variety of common working correlation structures. Methods are motivated using the ENGAGE study, a sequential multiple-assignment randomized trial aimed at developing a dynamic treatment regimen for increasing motivation to attend treatments among alcohol- and cocaine-dependent patients.

SMARTp: A SMART design for nonsurgical treatments of chronic periodontitis with spatially referenced and nonrandomly missing skewed outcomes.

This paper proposes dynamic treatment regimes (DTRs) as effective individualized treatment strategies for managing chronic periodontitis. The proposed DTRs are studied via SMARTp-a two-stage sequential multiple assignment randomized trial (SMART) design. For this design, we propose a statistical analysis plan and a novel cluster-level sample size calculation method that factors in typical features of periodontal responses such as non-Gaussianity, spatial clustering, and nonrandom missingness. Here, each patient is viewed as a cluster, and a tooth within a patient's mouth is viewed as an individual unit inside the cluster, with the tooth-level covariance structure described by a conditionally autoregressive structure. To accommodate possible skewness and tail behavior, the tooth-level clinical attachment level (CAL) response is assumed to be skew-t, with the nonrandomly missing structure captured via a shared parameter model corresponding to the missingness indicator. The proposed method considers mean comparison for the regimes with or without sharing an initial treatment, where the expected values and corresponding variances or covariance for the sample means of a pair of DTRs are derived by the inverse probability weighting and method of moments. Simulation studies are conducted to investigate the finite-sample performance of the proposed sample size formulas under a variety of outcome-generating scenarios. An R package SMARTp implementing our sample size formula is available at the Comprehensive R Archive Network for freedownload.

3193 Improving Individual Clinical Outcomes in a Sequential Multiple Assignment Randomized Trial (SMART)

OBJECTIVES/SPECIFIC AIMS: This work develops an algorithm that identifies patients in a Sequential Multiple Assignment Randomized Trial (SMART) who should switch treatments prior to the end of a stage because clinical effectiveness via their current intervention is unlikely. This algorithm uses as inputs patient baseline and interim measurements to assign a probability that a patient should switch or stay on their current intervention. First, the algorithm will be derived assuming both a linear and non-linear patient trajectory. Second, the performance of the algorithm will be assessed using trial data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care† (EMBARC) study. The primary objective of the algorithm is to switch treatment in patients who will not reach clinical effectiveness by the end of the stage, and the secondary objective is to avoid accidentally switching treatment in patients who will reach clinical effectiveness by the end of the stage. †Trivedi et al. Journal of Psychiatric Research 78 (2016) 11-23 METHODS/STUDY POPULATION: First, the algorithm was derived assuming a linear or non-linear trajectory. Next, performance of the algorithm was assessed using data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care† (EMBARC) study. This two-stage SMART design measured the effectiveness of sertraline in 242 patients with nonpsychotic Major Depressive Disorder (MDD). The algorithm was applied to baseline and interim measurements from the EMBARC study to predict end-stage Hamilton Depression (HAMD17) scores, the primary outcome of the study. True positive rate (TPR) and false positive rate (FPR) were used to measure respectively the primary study objective (switching treatment in patients who will not reach clinical effectiveness by the end of the stage), and the secondary study objective (avoiding accidentally switching treatment in patients who will reach clinical effectiveness by the end of the stage). TPR and FPR were calculated for the following prediction scenarios: (1) three separate two-point predictions: Baseline and Week 2, Baseline and Week 4, Baseline and Week 6, and (2) a single three-point prediction: Baseline and Weeks 2 and 6. †Trivedi et al. Journal of Psychiatric Research 78 (2016) 11-23 RESULTS/ANTICIPATED RESULTS: When using two-point prediction, we found TPR to increase and FPR to decrease as the interim measurements approached closer to the end of the stage. We also found TPR to increase when using a three-point prediction, but at the expense of FPR also increasing. Across these scenarios, TPR ranged between 70 and 90%, and FPR ranged between approximately 20 and 50%. DISCUSSION/SIGNIFICANCE OF IMPACT: Although SMART designs ultimately assign patients to more effective treatments, this process can take time and leave a patient (currently on an ineffective treatment) waiting until the end of a stage to try a potentially superior treatment. This disadvantage of the SMART design is currently addressed by this algorithm. By introducing a regression and likelihood approach to predict whether a patient should switch or stay on their current treatment, we move closer to the goal of designing rigorous, patient-centered studies. This work has the potential to improve individual clinical outcomes for patients enrolled in pragmatic clinical trials.

A SMART design to determine the optimal treatment of chronic pain among military personnel

Chronic pain is a leading cause of disability among active duty service members in the U.S. armed forces. Standard rehabilitative care and complementary and integrative health therapies are used for chronic pain rehabilitation. However, the optimal sequence and duration of these therapies has yet to be determined. This article describes a sequential multiple assignment randomized trial (SMART) protocol being used to identify the optimal components and sequence of standard rehabilitative care and complementary and integrative health therapies for reducing pain impact and improving other patient outcomes. Active duty service members referred to Madigan Army Medical Center for treatment of chronic pain are being recruited to the Determinants of the Optimal Dose and Sequence of Functional Restoration and Integrative Therapies study. Study participants are randomized to either standard rehabilitative care (physical and occupational therapy and psychoeducation) or complementary and integrative health therapies (chiropractic, acupuncture, yoga and psychoeducation). Those participants who do not respond to the first 3 weeks of treatment are randomized to receive an additional 3 weeks of either (1) the alternative treatment or (2) the first-stage treatment plus the alternative treatment. This study will also determine factors associated with treatment response that can support clinical decision making, such as baseline fitness, pain catastrophizing, kinesiophobia, post-traumatic stress, pain self-efficacy, and biological indicators. The information gained from this research will be applicable to all integrative chronic pain rehabilitation programs throughout the U.S. Department of Defense and the U.S. Department of Veterans Affairs, and the broader rehabilitation community.

Sequential, Multiple Assignment, Randomized Trial Designs in Immuno-oncology Research.

Clinical trials investigating immune checkpoint inhibitors have led to the approval of anti-CTLA-4 (cytotoxic T-lymphocyte antigen-4), anti-PD-1 (programmed death-1), and anti-PD-L1 (PD-ligand 1) drugs by the FDA for numerous tumor types. In the treatment of metastatic melanoma, combinations of checkpoint inhibitors are more effective than single-agent inhibitors, but combination immunotherapy is associated with increased frequency and severity of toxicity. There are questions about the use of combination immunotherapy or single-agent anti-PD-1 as initial therapy and the number of doses of either approach required to sustain a response. In this article, we describe a novel use of sequential, multiple assignment, randomized trial (SMART) design to evaluate immune checkpoint inhibitors to find treatment regimens that adapt within an individual based on intermediate response and lead to the longest overall survival. We provide a hypothetical example SMART design for BRAF wild-type metastatic melanoma as a framework for investigating immunotherapy treatment regimens. We compare implementing a SMART design to implementing multiple traditional randomized clinical trials. We illustrate the benefits of a SMART over traditional trial designs and acknowledge the complexity of a SMART. SMART designs may be an optimal way to find treatment strategies that yield durable response, longer survival, and lower toxicity. Clin Cancer Res; 24(4); 730-6. ©2017 AACR.

Using SMART design to improve symptom management among cancer patients: A study protocol.

In this in-progress sequential multiple assignment randomized trial (SMART), dyads of solid tumor cancer patients and their caregivers are initially randomized to 4 weeks of reflexology or meditative (mindfulness) practices provided by/with their caregiver in the patient's home or to a control group. After 4 weeks, intervention group dyads in which patients do not show improvement in fatigue (non-responders) are re-randomized to either receive additional time with the same therapy during weeks 5-8 or to add the other therapy. The aims are (1) to compare reflexology and meditative practices groups during weeks 1-4 on patients' fatigue severity, summed symptom inventory score, depressive symptoms, and anxiety, so as to determine the relative effectiveness of these therapies and the characteristics of responders and non-responders to each therapy. (2) Among reflexology non-responders based on fatigue score at week 4, to determine patient symptom outcomes when meditative practices are added during weeks 5-8, versus continuing with reflexology alone. (3) Among meditative practices non-responders based on fatigue score at week 4, to determine patient symptom outcomes when reflexology is added during weeks 5-8, versus continuing with meditative practices alone. (4) To compare improvements in patient symptom outcomes among the three groups created by the first randomization. (5) To explore which dyadic characteristics are associated with optimal patient symptom outcomes, to determine tailoring variables for decision rules of future interventions. The trial has a target of 331 dyads post-attrition and has 150 dyads enrolled. We are overcoming challenges with dyadic recruitment and retention while maintaining fidelity.

Design and analysis considerations for comparing dynamic treatment regimens with binary outcomes from sequential multiple assignment randomized trials

ABSTRACTIn behavioral, educational and medical practice, interventions are often personalized over time using strategies that are based on individual behaviors and characteristics and changes in symptoms, severity, or adherence that are a result of one's treatment. Such strategies that more closely mimic real practice, are known as dynamic treatment regimens (DTRs). A sequential multiple assignment randomized trial (SMART) is a multi-stage trial design that can be used to construct effective DTRs. This article reviews a simple to use ‘weighted and replicated’ estimation technique for comparing DTRs embedded in a SMART design using logistic regression for a binary, end-of-study outcome variable. Based on a Wald test that compares two embedded DTRs of interest from the ‘weighted and replicated’ regression model, a sample size calculation is presented with a corresponding user-friendly applet to aid in the process of designing a SMART. The analytic models and sample size calculations are presented for three of the more commonly used two-stage SMART designs. Simulations for the sample size calculation show the empirical power reaches expected levels. A data analysis example with corresponding code is presented in the appendix using data from a SMART developing an effective DTR in autism.