Sequential Multiple Assignment Randomized Trial Research Articles

Mobile phone-based smoking-cessation intervention in patients with chronic diseases in China: a Sequential Multiple Assignment Randomized Trial (SMART).

Mobile phone-based interventions show promise for smoking cessation but often face low engagement. This study assessed the feasibility and preliminary effectiveness of a 2-phase, multi-component mobile phone-based smoking cessation intervention for patients with chronic diseases. This Sequential Multiple Assignment Randomized Trial (SMART) recruited smokers with chronic diseases from hospitals in Zhuhai, China. Participants received brief cessation advice and were randomized to personalized chat support (PCS, n=64) or group chat support (GCS, n=64) for 1 month. At 1-month, PCS non-responders (continuing smokers) were re-randomized to receive either multi-component optional support (MOS) or continued PCS for 2 months. GCS non-responders were re-randomized to receive PCS or continued GCS. Responders continued with their initial intervention for 2 months. Feasibility outcomes included eligibility, participation, retention, intervention engagement, and satisfaction. Preliminary effectiveness was assessed by abstinence rates among non-responders. Of 160 screened, 128 (all male, 46.1% aged≤39 years) participated. At 1-month, 51 and 53 non-responded to PCS and GCS, respectively. Of them, 26 were re-randomized to receive PCS-MOS and 26 to receive GCS-PCS. At 6-month, PCS-MOS showed a non-significant higher validated abstinence rate compared to PCS alone (11.5% vs. 4.2%, odds ratio [OR] 3.13, 95%CI 0.30-32.31, P=0.34), GCS-PCS showed a non-significant lower validated abstinence rate compared to GCS (0% vs. 11.1%, OR 0.50, 95%CI 0.15-1.67, P=0.26). This trial showed the feasibility of leveraging hospital visits as teachable opportunities and integrating mobile phone-based smoking cessation support into chronic disease management in China. Optional treatments alongside mobile support showed preliminary effectiveness.

Prize-based Incentives for Smoking Cessation Among People with HIV: A Sequential Multiple Assignment Randomized Trial (SMART).

Contingency management (CM) is an incentive-based approach that has demonstrated efficacy for smoking cessation in various populations. There is an unmet need for feasible and effective smoking cessation interventions in people with HIV (PWH). The study purpose is to assess efficacy of prize-based CM for smoking cessation in PWH using a Sequential Multiple Assignment Randomization Trial (SMART) design selected to tailor intervention intensity based on early treatment response. During phase 1, 129 participants were randomly assigned to high-magnitude prize CM (HM-CM) or standard of care (SoC) for 4 weeks. Participants who did not reduce smoking were randomized in Phase 2 to continued counseling with HM-CM plus monitoring support or only continued monitoring support for 8 weeks. Participants who reduced smoking were randomized to booster monitoring with low-magnitude CM or no additional care. Outcomes were biochemically-verified smoking reduction and 7-day abstinence prevalence at post-treatment, 6-month and 12-month follow-up. Phase 1 responders (based on biochemical indicators of smoking reduction) were significantly less likely to return to smoking (during treatment and at 6- and 12-months) if they received low-magnitude incentives. Notably, initial exposure to CM vs. SoC did not increase rate of phase 1 response, and high-magnitude incentives later in treatment did not lead to greater smoking cessation for early treatment non-responders. Weekly CM sessions in the first four weeks of smoking cessation intervention did not perform significantly better than SoC. However, brief booster CM sessions aimed at maintaining early smoking cessation hold clinical promise and warrant further investigation. This represents the first trial to examine the use of contingency management for smoking cessation among people with HIV within the context of a Sequential Multiple Assignment, Randomized Trial (SMART) design.

SMART Designs: Bridging the Gap Between Clinical Trials and Practice in Infectious Diseases.

Traditional Randomized Controlled Trials often fall short in addressing the specific needs of clinical practice due to their one-size-fits-all treatment approaches. Sequential Multiple Assignment Randomized Trials (SMARTs) offer a dynamic and adaptive approach, allowing for multiple randomizations based on patient responses and evolving conditions. SMARTs enable personalized treatment pathways, such as in the trial for antiretroviral therapy (ART) in South Africa, which adjusts treatment based on patient outcomes. Despite these advantages, the use of SMARTs in infectious diseases remains limited. Greater adoption of SMARTs could promote more personalized treatment approaches, improve flexibility in response to public health needs, and enhance the effectiveness of interventions. However, challenges such as recruitment and increased expertise needed for more complex analyses must be addressed. Additionally, combining SMARTs with other adaptive designs could further improve the relevance and outcomes of clinical research.

Study protocol for a sequential multiple assignment randomized trial to decrease alcohol use before and after surgery

BackgroundHigh-risk alcohol consumption in the weeks before and after surgery poses significant risks for postoperative recovery. Despite this, elective surgical patients are rarely offered alcohol-focused education, interventions, or treatment. This paper describes the protocol of a research study designed to evaluate the effectiveness of brief, non-pharmacological, therapeutic approaches to reduce alcohol use before and after surgery. MethodsThe Alcohol Screening and Preoperative Intervention Research (ASPIRE-2) study trial includes 440 elective, non-cancer, surgery patients ages 21–75 years-old who report high-risk alcohol use. This trial uses a Sequential, Multiple Assignment, Randomized Trial (SMART) design to test the effectiveness of adaptive interventions that include preoperative Virtual Health Coaching (VHC) or Enhanced Usual Care (EUC) followed by postoperative intervention strategies tailored to participant response to the preoperative study condition. Intervention “response” is defined as achieving low-risk alcohol use following the preoperative intervention. The primary aims of this study are to: 1) examine the effectiveness of adaptive interventions that begin with preoperative VHC compared to EUC in reducing high-risk alcohol use among elective surgical patients; and 2) identify the most effective postoperative strategy for lasting alcohol use reduction over a period of 12 months. Secondary and exploratory aims will identify the best performing pre-specified adaptive interventions, identify baseline and time-varying moderators of intervention effectiveness, and evaluate surgical outcomes. ConclusionThe ASPIRE-2 study is an innovative approach to develop adaptive interventions to reduce alcohol use proximal to elective surgery when alcohol use poses short- and long-term risks to surgery and health.

The Adaptive Physical Activity Study for Stroke: A Feasibility Sequential Multiple Assignment Randomized Trial

Abstract Background Between 1990 and 2017, stroke cases nearly doubled, ranking as the second leading cause of death and disability globally. Inadequate physical activity (PA) is a leading risk factor. Recent guidelines advise stroke survivors to follow population-based PA guidelines. Given stroke heterogeneity personalised PA interventions are recommended. Therefore, this study used a Sequential Multiple-Assignment Randomized Trial (SMART) design to develop an adaptive mHealth intervention to improve PA post-stroke. Methods This SMART is in progress, with recruitment completed and follow-up data ending November 2024. A 12-week intervention programme was developed incorporating public patient involvement (PPI), empirical evidence and behavioural change theory to increase participation in Structured Exercise (SE), Lifestyle Physical Activity (LPA), or both. The study enrolled 54 ambulatory, community-dwelling people who were randomly assigned to either SE or LPA treatment groups. Participants used Fitbit Inspire 2 devices to monitor step counts and set personalized weekly step goals. At six weeks post-randomisation, participants were evaluated and classified as responders or non-responders based on their step count target achievement. Non-responders were re-randomized to switch treatments or augment their current regime with an additional intervention. Responders maintained their original treatment protocol. Primary outcomes address recruitment, retention and adherence rates. Secondary outcomes encompass overall health. Results Preliminary results include 54 participants (56% male) aged 51.58 (± 15.12) with Ischaemic (44%), Haemorrhagic (28%) or unknown (28%) stroke type at baseline. The mean weekly step was 7046 (±3797) while the mean Fatigue Severity Scale score was 38.02 (± 14.60). Participants had a mean Hospital Anxiety and Depression Scale score of 12.33 (± 6.98). Additionally, the mean Stroke Specific Quality of Life was 155.04 (58.84). Conclusion This is the first trial to use a SMART to design an adaptive intervention post-stroke internationally. Findings will inform the development of a PA intervention and subsequently will be evaluated against treatment as usual in a definitive trial.

Datopotamab-deruxtecan in early-stage breast cancer: the sequential multiple assignment randomized I-SPY2.2 phase 2 trial.

Among the goals of patient-centric care are the advancement of effective personalized treatment, while minimizing toxicity. The phase 2 I-SPY2.2 trial uses a neoadjuvant sequential therapy approach in breast cancer to further these goals, testing promising new agents while optimizing individual outcomes. Here we tested datopotamab-deruxtecan (Dato-DXd) in the I-SPY2.2 trial for patients with high-risk stage 2/3 breast cancer. I-SPY2.2 uses a sequential multiple assignment randomization trial design that includes three sequential blocks of biologically targeted neoadjuvant treatment: the experimental agent(s) (block A), a taxane-based regimen tailored to the tumor subtype (block B) and doxorubicin-cyclophosphamide (block C). Patients are randomized into arms consisting of different investigational block A treatments. Algorithms based on magnetic resonance imaging and core biopsy guide treatment redirection after each block, including the option of early surgical resection in patients predicted to have a high likelihood of pathological complete response, the primary endpoint. There are two primary efficacy analyses: after block A and across all blocks for the six prespecified breast cancer subtypes (defined by clinical hormone receptor/human epidermal growth factor receptor 2 (HER2) status and/or the response-predictive subtypes). We report results of 103 patients treated with Dato-DXd. While Dato-DXd did not meet the prespecified threshold for success (graduation) after block A in any subtype, the treatment strategy across all blocks graduated in the hormone receptor-negative HER2-Immune-DNA repair deficiency- subtype with an estimated pathological complete response rate of 41%. No new toxicities were observed, with stomatitis and ocular events occurring at low grades. Dato-DXd was particularly active in the hormone receptor-negative/HER2-Immune-DNA repair deficiency- signature, warranting further investigation, and was safe in other subtypes in patients who followed the treatment strategy. ClinicalTrials.gov registration: NCT01042379 .

Datopotamab-deruxtecan plus durvalumab in early-stage breast cancer: the sequential multiple assignment randomized I-SPY2.2 phase 2 trial.

Sequential adaptive trial designs can help accomplish the goals of personalized medicine, optimizing outcomes and avoiding unnecessary toxicity. Here we describe the results of incorporating a promising antibody-drug conjugate, datopotamab-deruxtecan (Dato-DXd) in combination with programmed cell death-ligand 1 inhibitor, durvalumab, as the first sequence of therapy in the I-SPY2.2 phase 2 neoadjuvant sequential multiple assignment randomization trial for high-risk stage 2/3 breast cancer. The trial includes three blocks of treatment, with initial randomization to different experimental agent(s) (block A), followed by a taxane-based regimen tailored to tumor subtype (block B), followed by doxorubicin-cyclophosphamide (block C). Subtype-specific algorithms based on magnetic resonance imaging volume change and core biopsy guide treatment redirection after each block, including the option of early surgical resection in patients predicted to have a high likelihood of pathologic complete response, which is the primary endpoint assessed when resection occurs. There are two primary efficacy analyses: after block A and across all blocks for six prespecified HER2-negative subtypes (defined by hormone receptor status and/or response-predictive subtypes). In total, 106 patients were treated with Dato-DXd/durvalumab in block A. In the immune-positive subtype, Dato-DXd/durvalumab exceeded the prespecified threshold for success (graduated) after block A; and across all blocks, pathologic complete response rates were equivalent to the rate expected for the standard of care (79%), but 54% achieved that result after Dato-DXd/durvalumab alone (block A) and 92% without doxorubicin-cyclophosphamide (after blocks A + B). The treatment strategy across all blocks graduated in the hormone-negative/immune-negative subtype. No new toxicities were observed. Stomatitis was the most common side effect in block A. No patients receiving block A treatment alone had adrenal insufficiency. Dato-DXd/durvalumab is a promising therapy combination that can eliminate standard chemotherapy in many patients, particularly the immune-positive subtype.ClinicalTrials.gov registration: NCT01042379 .

Improving Diabetes Equity and Advancing Care (IDEA) to optimize team-based care at a safety-net health system for Black and Latine patients living with diabetes: study protocol for a sequential, multiple assignment, randomized trial

BackgroundDiabetes is the eighth leading cause of death in the USA. Inequities driven by structural racism and systemic oppression have led to racial/ethnic disparities in diabetes prevalence, diagnosis, and treatment. Diabetes-self management training (DSMT), remote glucose monitoring (RGM), and tailored support from a community health worker (CHW) have the potential to improve outcomes. This study will examine the implementation of these interventions in a safety-net healthcare setting.MethodsUsing implementation science and racial equity principles, this study aims to (1) evaluate the appropriateness; (2) measure fidelity; and (3) compare the effectiveness of varying the combination and sequence of three interventions. An exploratory aim will measure sustainability of intervention adherence and uptake. This mixed-methods trial employs a sequential, multiple assignment randomized trial (SMART) design, patient focus group discussions, and staff interviews. Eligible Black/Latine patients will be recruited using patient lists extracted from the electronic medical record system. After a detailed screening process, eligible patients will be invited to attend an in-person enrollment appointment. Informed consent will be obtained and patients will be randomized to either DSMT or RGM. At 6 months, patients will complete two assessments (diabetes empowerment and diabetes-related distress), and HbA1c values will be reviewed. “Responders” will be considered those who have an HbA1c that has improved by at least one percentage point. “Responders” remain in their first assigned study arm. “Nonresponders” will be randomized to either switch study arms or be paired with a CHW. At 6 months participants will complete two assessments again, and their HbA1c will be reviewed. Twelve patient focus groups, two for each intervention paths, will be conducted along with staff interviews.DiscussionThis study is the first, to our knowledge, that seeks to fill critical gaps in our knowledge of optimal sequence and combinations of interventions to support diabetes management among Black and Latine patients receiving care at a safety-net hospital. By achieving the study aims, we will build the evidence for optimizing equitable diabetes management and ultimately reducing racial and ethnic healthcare disparities for patients living in disinvested urban settings.Trial registrationClinicalTrials.gov: NCT06040463. Registered on September 7, 2023.

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.

A sequential multiple assignment randomized trial of symptom management for cancer survivors during treatment and their informal caregivers.

Many cancer survivors and their informal caregivers experience multiple symptoms during the survivor's treatment. Test relative effectiveness and optimal sequencing of two evidence-based interventions for symptom management. In this sequential multiple assignment randomized trial (SMART), survivors of solid tumors with elevated depression or anxiety and their caregivers as dyads were initially randomized after baseline assessment in a 3:1 ratio to the Symptom Management and Survivorship Handbook (SMSH, N = 277 dyads) intervention or SMSH plus 8weeks of telephone interpersonal counseling (TIPC, N = 97 dyads). After 4weeks, survivors who were not responding (no improvement or worsening score on depression and/or anxiety item) to SMSH only and their caregivers were re-randomized to continue with SMSH alone (N = 44 dyads) to give it more time or to SMSH + TIPC (N = 44 dyads). Mixed effects and generalized linear models compared severity of depression, anxiety, and a summed index of 16 other symptoms over weeks 1-13 and week 17 between randomized groups and among three dynamic treatment regimes (DTRs). Dyads received SMSH only for 12weeks (DTR1); SMSH for 12weeks with 8weeks of TIPC added from week 1 (DTR2); and SMSH for 4weeks followed by the combined SMSH + TIPC for 8weeks if no response at 4weeks (DTR3). Survivors randomized initially to SMSH alone had significantly lower anxiety over weeks 1-13 compared to those randomized to the combined SMSH + TIPC. In comparing DTRs, survivor's anxiety was significantly lower at week 13 for DTR1 compared to DTR2 with no other main effects for survivors or caregivers. Exploratory moderation analyses indicated a potential benefit of adding TIPC for caregivers of non-responders with elevated baseline symptoms. SMSH + TIPC did not result in better symptom outcomes at week 17 than SMSH alone. Lower intensity SMSH may improve depression and anxiety symptoms for most survivors and their caregivers. Clinicaltrails.gov ID number, NCT03743415; approved and posted on 11/16/2018.

Does Being In-Person Matter? Demonstrating the Feasibility and Reliability of Fully Remote Observational Data Collection.

Many conventional research methods employed in randomized controlled trials were not possible during the height of the COVID-19 pandemic. In particular, behavioral observations are nearly universally gathered in-person. Observational methods are valued for the rich, informative data they produce in comparison to non-observational methods and are a cornerstone of parenting and family research. COVID provided the opportunity to, and indeed necessitated, the transition to fully remote observation. However, little to no studies have investigated whether remotely collected observational data are methodologically sound. This paper assesses the feasibility of remote data collection by describing the transition between in-person and fully remote observational data collection during a Sequential, Multiple Assignment, Randomized Trial (SMART) of a parenting program that took place both before and during the pandemic. Using mixed-methods data from coders, the overall quality of video-recorded data collected both before and during COVID was examined. Coder reliability over time was assessed with intraclass correlation coefficients. Results suggest that the frequency of audio problems, the severity of visual problems, and the level of administration challenges decreased after transitioning to remote data collection. Additionally, coders showed good to excellent reliability coding remotely collected data, and reliability even improved on some measured tasks. Although challenges to remote data collection exist, this study demonstrated that observational data can be collected feasibly and reliably. As observational data collection is a key method to assess parenting practices, these findings should improve researcher confidence in utilizing remote observational methods in prevention science.

A sequential, multiple assignment, randomized trial design with a tailoring function.

We present a trial design for sequential multiple assignment randomized trials (SMARTs) that use a tailoring function instead of a binary tailoring variable allowing for simultaneous development of the tailoring variable and estimation of dynamic treatment regimens (DTRs). We apply methods for developing DTRs from observational data: tree-based regression learning and Q-learning. We compare this to a balanced randomized SMART with equal re-randomization probabilities and a typical SMART design where re-randomization depends on a binary tailoring variable and DTRs are analyzed with weighted and replicated regression. This project addresses a gap in clinical trial methodology by presenting SMARTs where second stage treatment is based on a continuous outcome removing the need for a binary tailoring variable. We demonstrate that data from a SMART using a tailoring function can be used to efficiently estimate DTRs and is more flexible under varying scenarios than a SMART using a tailoring variable.

A generalized outcome-adaptive sequential multiple assignment randomized trial design.

A dynamic treatment regime (DTR) is a mathematical representation of a multistage decision process. When applied to sequential treatment selection in medical settings, DTRs are useful for identifying optimal therapies for chronic diseases such as AIDs, mental illnesses, substance abuse, and many cancers. Sequential multiple assignment randomized trials (SMARTs) provide a useful framework for constructing DTRs and providing unbiased between-DTR comparisons. A limitation of SMARTs is that they ignore data from past patients that may be useful for reducing the probability of exposing new patients to inferior treatments. In practice, this may result in decreased treatment adherence or dropouts. To address this problem, we propose a generalized outcome-adaptive (GO) SMART design that adaptively unbalances stage-specific randomization probabilities in favor of treatments observed to be more effective in previous patients. To correct for bias induced by outcome adaptive randomization, we propose G-estimators and inverse-probability-weighted estimators of DTR effects embedded in a GO-SMART and show analytically that they are consistent. We report simulation results showing that, compared to a SMART, Response-Adaptive SMART and SMART with adaptive randomization, a GO-SMART design treats significantly more patients with the optimal DTR and achieves a larger number of total responses while maintaining similar or better statistical power.

Rates of pathologic complete response (pCR) after datopotamab deruxtecan (Dato) plus durvalumab (Durva) in the neoadjuvant setting: Results from the I-SPY2.2 trial.

LBA501 Background: I-SPY2.2 is a multicenter phase 2 platform sequential multiple assignment randomized trial (SMART) in the neoadjuvant breast cancer setting that evaluates novel experimental regimens as first in a sequence (Block A) followed by standard chemo/targeted therapies (Blocks B/C) if indicated. The goal is to achieve a pCR after novel targeted agents alone or in sequence with standard therapies, with the optimal therapy assigned based on the tumor response predictive subtype (RPS). RPS incorporates expression-based immune, DNA repair deficiency (DRD), and luminal signatures with hormone receptor (HR) and HER2 status to subset patients into 6 subtypes: S1: HR+HER2-Immune-DRD-; S2: HR-HER2-Immune-DRD-; S3: HER2-Immune+; S4: HER2-Immune-DRD+; S5: HER2+/non-Luminal; S6: HER2+/Luminal. Methods: RPS S1, S2, S3, and S4 were eligible for assignment to Dato+Durva in Block A. Patients were followed by MRI during treatment (at 3, 6, and 12 weeks after start of Blocks A and B). Predicted responders by MRI and biopsy at the end of Block A or B have the option of going to surgery early; otherwise, they proceed to next treatment Block (B +/- C). Randomization to Block B includes a taxane-based regimen specific to the RPS, and options include S1: paclitaxel; S2 and S3: paclitaxel + carboplatin + pembrolizumab; S4: paclitaxel + carboplatin vs. paclitaxel + carboplatin + pembrolizumab. Patients who did not go to surgery after Block B proceeded to Block C (AC or AC + Pembrolizumab if HR-HER2-). The primary endpoint is pCR. Efficacy is evaluated within each RPS and HR+HER2- and HR-HER2- signatures. To estimate the arm's efficacy as a stand-alone therapy, we use a Bayesian covariate-adjusted model to estimate the pCR rate and compare the posterior distribution to a subtype-specific fixed threshold. This model uses pCR data when available and MRI data when pCR is not. To estimate pCR rate in the context of a multi-decision treatment regimen, we use a Bayesian model based on if and when a pCR occurred in the trial. The posterior is compared to a subtype-specific dynamic control generated from historical I-SPY data. Results: 106 patients were randomly assigned to the Dato+Durva arm between September 2022 and August 2023. The results for Dato+Durva as a stand-alone therapy are summarized in Table. After completion of Block A, 36 patients proceeded to surgery without completing Blocks B/C. Conclusions: Dato+Durva meets threshold for graduation within the RPS S3 subtype based on estimated pCR rate of 72% and warrants further investigation in a larger randomized controlled trial. Clinical trial information: NCT01042379 . [Table: see text]

Rates of pathologic complete response (pCR)after neoadjuvant datopotamab deruxtecan (Dato): Results from the I-SPY2.2 trial.

LBA509 Background: I-SPY2.2 is a multicenter phase 2 platform sequential multiple assignment randomized trial (SMART) evaluating novel experimental regimens in the neoadjuvant breast cancer setting. The novel therapy is given as first in a sequence (Block A), followed by standard chemo/targeted therapies (Block B/C) if indicated. The goal is to identify agents that lead to pCR after novel targeted agents alone, or in sequence with optimal therapy assigned based on the tumor response predictive subtype (RPS). RPS incorporates expression-based immune, DNA repair deficiency (DRD), and luminal signatures with hormone receptor (HR) and HER2 status to classify patients by subtype: S1: HR+HER2-Immune-DRD-; S2: HR-HER2-Immune-DRD-; S3: HER2-Immune+; S4: HER2-Immune-DRD+; S5: HER2+/non-Luminal; S6: HER2+/Luminal. Methods: RPS S1, S2, S3 and S4 were eligible for assignment to Dato in Block A. Patients (pts) were followed by MRI during treatment (at 3, 6 and 12 weeks after start of Blocks A and B). Predicted responders by MRI and biopsy at the end of Block A or B have the option of going to surgery early, otherwise they proceed to next treatment Block (B +/- C). Randomization to Block B includes a taxane-based regimen specific to the RPS, and options include S1: paclitaxel; S2 and S3: paclitaxel + carboplatin + pembrolizumab; S4: paclitaxel + carboplatin vs. paclitaxel + carboplatin + pembrolizumab. Patients who did not go to surgery after Block B proceeded to Block C (AC or AC + Pembrolizumab if HR-HER2-). The primary endpoint is pCR. Efficacy is evaluated within each RPS and HR+HER2- and HR-HER2- signatures. To estimate the arm's efficacy as a stand-alone therapy, we use a Bayesian covariate-adjusted model to estimate the pCR rate and compare the posterior distribution to a subtype-specific fixed threshold. This model uses pCR data when available and MRI data when pCR is not. To estimate pCR rate in the context of a multi-decision treatment regimen, we use a Bayesian model based on if and when a pCR occurred in the trial. The posterior is compared to a subtype-specific dynamic control generated from historical I-SPY data. Results: 103 pts were randomly assigned to the Dato arm between August 2022 and August 2023. All patients have proceeded beyond Block A; 33 went to surgery after Dato alone. The efficacy results for Dato as a stand-alone therapy are summarized in Table. Conclusions: Dato monotherapy was active, particularly in the HR-HER2- signature, but did not meet the pre-specified threshold for graduation in I-SPY 2.2. Clinical trial information: NCT01042379 . [Table: see text]

Modeling any potential change in size, cost or patient benefit, resulting from incorporation of ctDNA, or other “optimizing diagnostic,” as part of a novel “SMARTer” oncology registration trial design.

e23027 Background: One limitation in speeding more effective new cancer therapies to patients, and saving lives, is the time and expense of clinical trials. A second limitation is cancer is often a chronic disease, sequentially treated with multiple therapies; unlike in most registration trials, patient progress through a series of therapies. Building on previously published Sequential Multiple Assignment Randomized Trial (SMART) design, herein we model a new design to enhance registration (“SMARTer”). An “optimizing diagnostic” (often a ctDNA test) is used to determine, soon after initial use, if the new therapy is working. Those failing it are switched to the Standard of Care (SOC) for analysis they remain in the “new therapy first” arm, which is compared to the SOC arm. The aim is for all patients who benefit from the new therapy to remain on it, and, in contrast, if they are doomed to fail it, switch to SOC promptly to receive benefit from SOC early in the therapy path. Methods: To determine the value of the “new therapy first” arm we compared it to SOC by modeling assumptions of the accuracy of the optimizing test and the level of benefit of SOC after new therapy. The benefits of a new therapy first and switching, vs. SOC, was compared to a standard new therapy vs SOC “head-to-head”, without switching. Six scenarios, varying the effectiveness of the therapies, each with 16 combinations of sensitivity and specificity, were modeled. Results: Surprisingly, a good optimizing test reduces the number of patients by as much as 84% (from 710 to 114 in total) when new therapy alone had a 40% response rate and SOC a 30% rate (see table). When new therapy alone was no better than SOC, a study of 104 patients was sufficient to document that using the new therapy first added to patient benefit and could save lives, assuming independent mechanisms. Conclusions: SMARTer design thus significantly increases chances of approval, better mimics patients’ real world therapy path, and would reduce both time and cost significantly. An Optimizing Diagnostic, predicting appropriateness of a second dose/round of therapy based on the benefit of the first dose/round, thus facilitates clinical trials in a manner analogous to a Companian Diagnostic predicting the appropriateness of starting the first dose of a new therapy. [Table: see text]

Approaches to Statistical Efficiency When Comparing the Embedded Adaptive Interventions in a SMART

Sequential, multiple assignment randomized trials (SMARTs), which assist in the optimization of adaptive interventions, are growing in popularity in education and behavioral sciences. This is unsurprising, as adaptive interventions reflect the sequential, tailored nature of learning in a classroom or school. Nonetheless, as is true elsewhere in education research, observed effect sizes in education-based SMARTs are frequently small. As a consequence, statistical efficiency is of paramount importance in their analysis. The contributions of this manuscript are twofold. First, we provide an overview of adaptive interventions and SMART designs for researchers in education science. Second, we propose four techniques that have the potential to improve statistical efficiency in the analysis of SMARTs. We demonstrate the benefits of these techniques in SMART settings both through the analysis of a SMART designed to optimize an adaptive intervention for increasing cognitive behavioral therapy delivery in school settings and through a comprehensive simulation study. Each of the proposed techniques is easily implementable, either with over-the-counter statistical software or through R code provided in Supplemental Material.

Optimizing mHealth Instant Messaging-Based Smoking Cessation Support: A Sequential, Multiple Assignment, Randomized Trial (SMART)

Optimizing mHealth Instant Messaging-Based Smoking Cessation Support: A Sequential, Multiple Assignment, Randomized Trial (SMART)

A novel experimental vignette methodology: SMART vignettes

We motivate and present the methodology of vignette studies. The primary contribution of this paper is our proposal of a novel vignette study design: “SMART vignettes.” Our design has two notable features: the first is its use of sequential randomization, which conceptually originates from the sequential multiple assignment randomization trial (SMART) design developed by Murphy (2004). The second feature is adaptive allocation. These new features in vignette studies offer unique advantages not offered by traditional vignettes: (1) valid causal inferences on the conditional distributions of the primary outcome of interest, given other factors, (2) balanced allocations across groups, and (3) a greater degree of interactivity for the survey respondent. We illustrate the utility of our method using a case example of a vignette study used to probe physicians’ attitudes toward an AI-embedded clinical system. In this example, a SMART vignette was used to randomize hypothetical scenarios to gain a better understanding of the causal impact of physician attitudes, given emerging evidence that a range of factors including previous decisions, play a role in influencing clinical decisions. We simulated hypothetical vignette studies under both SMART and conventional (i.e. single randomization at baseline) designs. We varied the number of factors for each study and fixed each factor to have two levels. Relative loss was used to compare the degree of imbalance between groups. Both designs had smaller relative losses with larger sample sizes. The SMART study design had lower loss than its conventional counterpart for all values of [Formula: see text] for all studies, indicating better balance. As demonstrated by the relative loss in our simulations, our proposed SMART vignette design has an advantage over the conventional design. This method holds promise in generating new knowledge in decision making scenarios occurring over multiple and discrete time points.

Understanding Physician's Perspectives on AI in Health Care: Protocol for a Sequential Multiple Assignment Randomized Vignette Study.

As the availability and performance of artificial intelligence (AI)-based clinical decision support (CDS) systems improve, physicians and other care providers poised to be on the front lines will be increasingly tasked with using these tools in patient care and incorporating their outputs into clinical decision-making processes. Vignette studies provide a means to explore emerging hypotheses regarding how context-specific factors, such as clinical risk, the amount of information provided about the AI, and the AI result, may impact physician acceptance and use of AI-based CDS tools. To best anticipate how such factors influence the decision-making of frontline physicians in clinical scenarios involving AI decision-support tools, hypothesis-driven research is needed that enables scenario testing before the implementation and deployment of these tools. This study's objectives are to (1) design an original, web-based vignette-based survey that features hypothetical scenarios based on emerging or real-world applications of AI-based CDS systems that will vary systematically by features related to clinical risk, the amount of information provided about the AI, and the AI result; and (2) test and determine causal effects of specific factors on the judgments and perceptions salient to physicians' clinical decision-making. US-based physicians with specialties in family or internal medicine will be recruited through email and mail (target n=420). Through a web-based survey, participants will be randomized to a 3-part "sequential multiple assignment randomization trial (SMART) vignette" detailing a hypothetical clinical scenario involving an AI decision support tool. The SMART vignette design is similar to the SMART design but adapted to a survey design. Each respondent will be randomly assigned to 1 of the possible vignette variations of the factors we are testing at each stage, which include the level of clinical risk, the amount of information provided about the AI, and the certainty of the AI output. Respondents will be given questions regarding their hypothetical decision-making in response to the hypothetical scenarios. The study is currently in progress and data collection is anticipated to be completed in 2024. The web-based vignette study will provide information on how contextual factors such as clinical risk, the amount of information provided about an AI tool, and the AI result influence physicians' reactions to hypothetical scenarios that are based on emerging applications of AI in frontline health care settings. Our newly proposed "SMART vignette" design offers several benefits not afforded by the extensively used traditional vignette design, due to the 2 aforementioned features. These advantages are (1) increased validity of analyses targeted at understanding the impact of a factor on the decision outcome, given previous outcomes and other contextual factors; and (2) balanced sample sizes across groups. This study will generate a better understanding of physician decision-making within this context. DERR1-10.2196/54787.