Displaying 11 trials out of 11.
Authors: T. Badger, C. Segrin, T.E. Crane, P. Chalasani, W. Arslan, M. Hadeed, C.W. Given, A. Sikorskii
Year: 2024
Sample size: 374
Context: Many cancer survivors and their informal caregivers experience multiple symptoms during the survivor’s treatment. Objective: Test relative effectiveness and optimal sequencing of two evidence-based interventions for symptom management. Methods: 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 8 weeks of telephone interpersonal counseling (TIPC, N = 97 dyads). After 4 weeks, 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 12 weeks (DTR1); SMSH for 12 weeks with 8 weeks of TIPC added from week 1 (DTR2); and SMSH for 4 weeks followed by the combined SMSH + TIPC for 8 weeks if no response at 4 weeks (DTR3). Results: 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. Conclusion: 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. Trial registration: Clinicaltrails.gov ID number, NCT03743415; approved and posted on 11/16/2018.
Identifiability Efforts: SUTVA and possible interference was addressed by cluster-level (dyad) randomisation. Exchangeability and positivity are implied due to the use of randomisation and IPW. The ICE of non-response was handled by being incorporated into the DTR.
Pitfalls: Attrition analysis was performed to test for informative drop-out.
Authors: K.M. Ellison, A. El Zein, N.K. Baidwan, C.C. Ferguson, L.A. Fowler, D.R. Bryan, C. Reynolds, D. Hermanson, K.J. Berg, T. Mehta, J.O. Hill, H.R. Wyatt, R.D. Sayer
Year: 2025
Sample size: 83
Background: Dietary carbohydrate restriction, time-restricted eating (TRE), and exercise are common strategies for weight loss and improving glycemic control. However, the optimal combination and sequence of these strategies is unclear. Objectives: We investigated adaptive treatment strategies for weight loss and improving cardiometabolic health in adults with overweight or obesity (BMI [in kg/m2] ≥27) and prediabetes. Methods: ADAPT was a 16-wk group-based weight loss Sequential Multiple Assignment Randomized Trial. In total, 83 adults were initially randomized to either a calorie-restricted reduced carbohydrate (RC) diet or high-carbohydrate (HC) diet. Nonresponders (<2.5% weight loss at week 4) were rerandomly assigned to augment initial dietary prescriptions with either TRE or exercise counseling. Results: Of 82 participants (53.8 ± 11.7 y; 84.3% females; BMI: 38.3 ± 7.2) who completed week 4 assessments, 46 (55.4%) were nonresponders and rerandomly assigned to TRE (n = 22) or exercise (n = 24). Weight loss at week 16 was similar between HC and RC (0.15 kg; 95% CI: −1.86, 2.16 kg; P = 0.88). Although the HC group showed greater improvements in fasting glucose, (−8.0 mg/dL; 95% CI: −15.29, −0.67 mg/dL), changes in A1c, fasting insulin, and quantitative insulin sensitivity check index were not different between HC and RC. Among nonresponders, assignment to second-stage interventions of TRE or exercise did not differentially affect changes in any study outcomes, and less weight loss was achieved among early nonresponders compared with responders despite the addition of TRE or exercise. Conclusions: The group-based program resulted in clinically significant weight loss that was similar between calorie-restricted HC and RC diets. However, counseling to follow a HC diet reduced fasting glucose compared with RC. Patients with obesity and prediabetes who are unable to achieve early weight loss may require more intensive and costly intervention strategies (i.e., meal provisions and supervised exercise) to improve obesity treatment outcomes. This trial was registered at ClinicalTrials.gov as NCT04745572 (https://clinicaltrials.gov/study/NCT04745572?term=NCT04745572&rank=1).
Identifiability Efforts: Exchangeability and positivity are implied due to the use of stratified randomisation. Only non-responders were re-randomised leading to overrepresentation of responders in certain treatment arms - this was corrected by IPW. SUTVA was not explicitly addressed but interference due to participants sharing the same interventionist - this was solved by having one person administer any given treatment thus leading to intervention-induced clustering. The authors list relevant clinical/adverse events and handled by incorporating events related to non-adherence and contra-indications into the protocol, effectively extending the DTR. ICE relating to non-response were handled by re-randomisation as part of the DTR.
Pitfalls: Adherence was not consistently monitored, forcing the authors to pursue a treatment-policy strategy to account for non-adherence. However, while the treatment-policy strategy requires following up on drop-outs, the authors performed a complete-case analysis. The authors did not address whether dropout (10 out of 83 participants) was informative, and did not investigate reasons for missingness.
Authors: R. Levy, M. Mathai, P. Chatterjee, L. Ongeri, S. Njuguna, D. Onyango, D. Akena, G. Rota, A. Otieno, T.C. Neylan, H. Lukwata, J.G. Kahn, C.R. Cohen, D. Bukusi, G.A. Aarons, R. Burger, K. Blum, I. Nahum-Shani, C.E. McCulloch, S.M. Meffert
Year: 2019
Sample size: 2710
Background: Mental disorders are a leading cause of global disability, driven primarily by depression and anxiety. Most of the disease burden is in Low and Middle Income Countries (LMICs), where 75% of adults with mental disorders have no service access. Our research team has worked in western Kenya for nearly ten years. Primary care populations in Kenya have high prevalence of Major Depressive Disorder (MDD) and Posttraumatic Stress Disorder (PTSD). To address these treatment needs with a sustainable, scalable mental health care strategy, we are partnering with local and national mental health stakeholders in Kenya and Uganda to identify 1) evidence-based strategies for first-line and second-line treatment delivered by non-specialists integrated with primary care, 2) investigate presumed mediators of treatment outcome and 3) determine patient-level moderators of treatment effect to inform personalized, resource-efficient, non-specialist treatments and sequencing, with costing analyses. Our implementation approach is guided by the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. Methods/design: We will use a Sequential, Multiple Assignment Randomized Trial (SMART) to randomize 2710 patients from the outpatient clinics at Kisumu County Hospital (KCH) who have MDD, PTSD or both to either 12 weekly sessions of non-specialist-delivered Interpersonal Psychotherapy (IPT) or to 6 months of fluoxetine prescribed by a nurse or clinical officer. Participants who are not in remission at the conclusion of treatment will be re-randomized to receive the other treatment (IPT receives fluoxetine and vice versa) or to combination treatment (IPT and fluoxetine). The SMART-DAPPER Implementation Resource Team, (IRT) will drive the application of the EPIS model and adaptations during the course of the study to optimize the relevance of the data for generalizability and scale-up. Discussion: The results of this research will be significant in three ways: 1) they will determine the effectiveness of non-specialist delivered first- A nd second-line treatment for MDD and/or PTSD, 2) they will investigate key mechanisms of action for each treatment and 3) they will produce tailored adaptive treatment strategies essential for optimal sequencing of treatment for MDD and/or PTSD in low resource settings with associated cost information- A critical gap for addressing a leading global cause of disability.
Identifiability Efforts: The authors state that they expect stratified randomisation for equal group size and gender at both allocation stages to ensure ‘balance for other variables’, implying exchangeability and positivity. Additionally, clinical evaluators will be blinded, but not participants and treatment providers. The authors state they use a treatment policy strategy that requires attendance of at least one treatment session (treatment policy strategy). While it is a common pitfall in the treatment policy strategy to not follow-up on patients that deviate from the protocol, they do state that this is a ‘modified‘ intention-to-treat analysis. However, they do not address the potential selection bias this might introduce as treatment initiators might differ from those that don’t initiate treatment. They do intend to measure adherence (e.g., by sampling charts of treated patients). The possibility of interference was not addressed, but seems unlikely in an outpatient setting.
Pitfalls: The authors intend to use Q-learning but did not assess the plausibility of the Markov property. Furthermore, the authors did not anticipate (or plan to assess) reasons for missingness. The authors state that patients who start a new medical treatment during the study will be treated as dropouts. This will likely cause informative censoring, however, the authors do not address this.
Authors: G.R. Lyden, D.M. Vock, A. Sur, N. Morrell, C.M. Lee, M.E. Patrick
Year: 2022
Sample size: 891
M-bridge was a sequential multiple assignment randomized trial (SMART) that aimed to develop a resource-efficient adaptive preventive intervention (API) to reduce binge drinking in first-year college students. The main results of M-bridge suggested no difference, on average, in binge drinking between students randomized to APIs versus assessment-only control, but certain elements of the API were beneficial for at-risk subgroups. This paper extends the main results of M-bridge through an exploratory analysis using Q-learning, a novel algorithm from the computer science literature. Specifically, we sought to further tailor the two aspects of the M-bridge APIs to an individual and test whether deep tailoring offers a benefit over assessment-only control. Q-learning is a method to estimate decision rules that assign optimal treatment (i.e., to minimize binge drinking) based on student characteristics. For the first aspect of the M-bridge API (when to offer), we identified the optimal tailoring characteristic post hoc from a set of 20 candidate variables. For the second (how to bridge), we used a known effect modifier from the trial. The results of our analysis are two rules that optimize (1) the timing of universal intervention for each student based on their motives for drinking and (2) the bridging strategy to indicated interventions (i.e., among those who continue to drink heavily mid-semester) based on mid-semester binge drinking frequency. We estimate that this newly tailored API, if offered to all first-year students, would reduce binge drinking by 1 occasion per 2.5 months (95% CI: decrease of 1.45 to 0.28 occasions, p < 0.01) on average. Our analyses demonstrate a real-world implementation of Q-learning for a substantive purpose, and, if replicable in future trials, our results have practical implications for college campuses aiming to reduce student binge drinking.
Identifiability Efforts: Exchangeability is implied due to utilising SMART data and modeling treatment allocation probability in the value function through IPW. Positivity is not addressed - it is noteworthy that they considered 20 tailoring variables, meaning that sufficient data must be available for each considered DTR.
Pitfalls: The possibility of interference in a cohort of 900 students is not unlikely but was not addressed by the authors. The authors did not specify from which university the students were sampled making it unknown from the article itself whether cluster-level interference is possible. They did not address the Markov property while using Q-learning which is reliant on this assumption. The authors did not describe the extent of censoring and whether censoring (or outcome-missingness) was informative or not: they did state that missingness was handled by using multiple imputation. The authors did not report adherence measures, which is relevant when testing the optimal sequence of treatment assignment: the lower the adherence, the larger the discrepancy between the effect of treatment-assignment and treatment-compliance. The authors did not report possible reasons for missingness.
Authors: A. Sikorskii, T. Badger, C. Segrin, T.E. Crane, P. Chalasani, W. Arslan, M. Hadeed, K.E. Morrill, C. Given
Year: 2023
Sample size: 451
Context: Many cancer survivors experience a lingering symptom burden after chemotherapy. Objectives: In this sequential multiple assignment randomized trial, we tested optimal sequencing of two evidence-based interventions for symptom management. Methods: Survivors of solid tumors (N = 451) were interviewed at baseline and stratified as high or low need for symptom management based on comorbidity and depressive symptoms. High need survivors were randomized initially to the 12-week Symptom Management and Survivorship Handbook (SMSH, N = 282) or 12-week SMSH with eight weeks of Telephone Interpersonal Counseling (TIPC, N = 93) added during weeks one to eight. After four weeks of the SMSH alone, non-responders on depression were re-randomized to continue with SMSH alone (N = 30) or add TIPC (N = 31). Severity of depression and summed severity index of 17 other symptoms over weeks one to13 were compared between randomized groups and among three dynamic treatment regimes (DTRs): 1) SMSH for 12 weeks; 2) SMSH for 12 weeks with eight weeks of TIPC from week one; 3) SMSH for four weeks followed by SMSH+TIPC for eight weeks if no response to the SMSH alone on depression at week four. Results: There were no main effects for randomized arms or DTRs, but there was a significant interaction of trial arm with baseline depression favoring SMSH alone during weeks one to four in the first randomization and SMSH+TIPC in the second randomization. Conclusion: The SMSH may represent a simple effective option for symptom management, adding TIPC only when there is no response to SMSH alone for people with elevated depression and multiple co-morbidities.
Identifiability Efforts: Exchangeability and positivity are implied due to the use of randomisation (the authors only compared regimes already embedded in the SMART). Additionally they adjusted for baseline values of outcomes and applied IPW to further ensure exchangeability. Furthermore, they reported group difference after randomisation and response rates per treatment condition. The DTR strategy was used to handle the ICE of non-response to treatment. The authors did perform attrition analysis to check whether drop-out led to selection bias. Possibility of interference was not explicitly addressed, but seems unlikely in this setting.
Pitfalls: They did address informative censoring and reported response rates. Reasons for missingness are implied to be non-informative, as they report drop-outs to be exchangeable based on baseline-covariates.
Authors: C. Yu, D. Bian, J. Zhang, X. Han, C. Shi, G. Li
Year: 2025
Sample size: 92
Background: Nonpharmacological interventions are important prevention strategies for mild cognitive impairment (MCI), but effects vary significantly between individuals based on personal characteristics, while current practice relies on experience-based approaches lacking personalized, adaptive intervention strategies. Objective: The objective of our study was to develop and evaluate evidence-based adaptive intervention strategies for optimizing cognitive function among older adults with MCI using a Sequential Multiple Assignment Randomized Trial (SMART) design, comparing the effectiveness of cognitive training (CT) combined with virtual reality Taichi (VRTC) versus offline Taichi (OffTC) versus control, and to identify baseline characteristics that predict treatment response for personalized intervention delivery. Methods: We recruited 92 community-dwelling adults aged ≥60 years diagnosed with MCI from 3 districts in Shanghai, China. A 24-week SMART was conducted between April and December 2023. During the first stage (weeks 1-12), participants were randomly assigned to control (n=26) or intervention groups receiving CT combined with either OffTC (n=33) or VRTC (n=34). Nonresponders at week 12 were rerandomized to alternative or intensified interventions during the second stage (weeks 13-24). The primary outcome was the Memory Guard score (MGs) at 24 weeks. Dynamic treatment regimen analysis assessed optimal adaptive strategies using regression models. Results: A total of 81 participants completed the trial. CT+VRTC demonstrated significantly superior cognitive improvement compared to control (5.10 MGs, 95% CI 2.93-7.27; Cohen d=1.425, 95% CI 0.785-2.060; P<.001) and CT+OffTC (3.61 MGs, 95% CI 1.71-5.51; Cohen d=1.009, 95% CI 0.461-1.560; P<.001). At 24 weeks, adjusted mean MGs were: CT+VRTC 32.9 (95% CI 31.3-34.5), CT+OffTC 29.3 (95% CI 27.7-30.9), and control 27.8 (95% CI 26.0-29.6). Dynamic treatment regimen analysis revealed VRTC-based adaptive strategies consistently outperformed static approaches, with VRTC responders achieving the highest effectiveness (5.40 MGs improvement, 95% CI 3.10-7.70; P<.001). Treatment intensification proved more effective than modality switching for nonresponders. Subgroup analyses revealed that younger participants (≤71 years), individuals with lower baseline cognitive function, and those with comorbid conditions demonstrated enhanced responsiveness, suggesting these populations may derive greater benefit from virtual reality–based approaches. Conclusions: This SMART trial established the first evidence-based adaptive intervention framework for community MCI prevention, demonstrating that virtual reality–enhanced Taichi combined with CT produced superior outcomes compared with traditional exercise and control conditions. Treatment intensification proved more effective than modality switching for nonresponders. Diabetes showed a better response enabling personalized intervention selection. These findings provide clinicians with objective decision rules for treatment adaptation and identify high-benefit populations (younger adults, those with lower baseline cognition, and patients with metabolic comorbidities) for targeted intervention. The protocol can be implemented by community health workers within existing urban health care infrastructure, offering a scalable approach to precision-based cognitive health management in aging populations.
Identifiability Efforts: Positivity is implied due to the use of randomisation. Furthermore, the authors selected confounders based on existing literature therefore explicitly addressing exchangeability. The authors explicitly specify that they calculate an intention-to-treat effect. ICE due to non-response were handled by incorporating them as a tailouring variable in the DTRs. Cluster sampling was used which can account for possible interference within clusters.
Pitfalls: Noteworthy is that 30 eligible participants declined participation due to Covid-related concerns relating to in-person study visits, which might introduce a sampling bias. The authors did not assess whether drop-out was informative and did not provide adherence measures, which is important for an accurate interpretation of the intention-to-treat effect. Since drop-out did occur, calculating the intention-to-treat effect might lead to bias assuming that drop-out is not missing-completely-at-random. Reasons for missingness were not investigated.
Authors: Kyla Blalock, Jacqueline Pistorello, Shireen L Rizvi, John R Seeley, Francesca Kassing, James Sinclair, Linda A Oshin, Robert J Gallop, Cassidy M Fry, Ted Snyderman, David A Jobes, Jennifer Crumlish, Hannah R Krall, Susan Stadelman, Filiz Gözenman-Sapin, Kate Davies, David Steele, David B Goldston, Scott N Compton
Year: 2025
Sample size: 480
Background Suicidal ideation is increasing among university students. Despite growing demand for services, university counseling centers (UCCs) face limited resources to meet the complex needs of students who are suicidal. Objective The Comprehensive Adaptive Multisite Prevention of University Student Suicide (CAMPUS) Trial evaluates 4 treatment sequences within UCCs to develop evidence-based treatment guidelines. Methods The CAMPUS Trial consists of a feasibility study followed by a sequential multiple-assignment randomized trial (SMART). The original CAMPUS protocol was modified during the COVID-19 pandemic to accommodate new UCC tele–mental health services, including remote treatment, assessments, and monitoring. A smaller-scale feasibility study was conducted to (1) evaluate implementation of hybrid telehealth and in-person interventions and (2) fine-tune online procedures. Following the feasibility study, university students (aged 18-25 years) seeking UCC services with moderate to severe suicidal ideation will enroll in the CAMPUS Trial. Student participants are randomly assigned to 1 of 4 treatment sequences with 2 stages of intervention. In stage 1, students receive 4 to 6 weeks of either (1) a suicide-focused treatment—Collaborative Assessment and Management of Suicidality—or (2) enhanced treatment as usual. Treatment responders enter the maintenance phase. In stage 2, nonresponders are rerandomized for an additional 1 to 8 weeks of (1) Collaborative Assessment and Management of Suicidality or (2) an intensive skills-based treatment—dialectical behavior therapy for UCC settings. UCC counselors will enroll in the CAMPUS Trial to complete measures about their experience working with students who are suicidal. CAMPUS Trial administration includes representation from all sites to facilitate cross-site coordination and an advisory board of stakeholders from all UCCs to facilitate treatment implementation. Results Student participant recruitment began on October 25, 2022, and ended on May 16, 2024. As of November 2024, data collection for the SMART was ongoing with active study participants. Data collection was completed in November 2024, and as of April 2025, data analysis is underway. Full results will be available in 2025. Conclusions The CAMPUS Trial offers a model for future SMARTs for the treatment of suicidal thoughts or behaviors (or both) across various settings. The results will inform treatment guidelines for students presenting with suicidality at UCCs. Trial Registration ClinicalTrials.gov NCT04707066; http://clinicaltrials.gov/ct2/show/NCT04707066 International Registered Report Identifier (IRRID) DERR1-10.2196/68441
Identifiability Efforts: Exchangeability and positivity are implied due to the use of stratified randomisation. Due to the sensitive nature of the problem (suicidal behaviour), interventions were highly dynamic to adapt to progression and non-response.
Pitfalls: The authors do intend to assess adherence. They do not state intent to handle informative censoring should this occur. The possibility of interference is not implausible as students are recruited from one of four universities. The authors do not state intent to investigate reasons for missingness.
Authors: Claire See Ying Chang, Vanessa Phua, Xiang Cong Tham, Yuanxi Jia, Nicole Yun Ching Chen, Wentao Zhou, Wei Fong Liau, Jing Xu, Bibhas Chakraborty, Nicholas Graves, Nick Sevdalis, Yanhong Dong
Year: 2025
Sample size: 160
Introduction Middle-aged adults with chronic conditions including diabetes, hyperlipidaemia and hypertension are at higher risk for cognitive decline. However, there is a lack of a targeted solution for this population. This study aims to develop a digital solution for salutogenic brain health targeting this population, assess its clinical effectiveness and evaluate the implementation in primary care settings by local champions, that is, nurses. Methods and analysis A type-I hybrid effectiveness-implementation design with a sequential multiple-assignment randomised trial will be adopted. 160 adults aged 40–64 years old with chronic conditions hypertension, hyperlipidaemia and type-II diabetes will be recruited from three National University Polyclinics in Singapore. They will be randomised using block randomisation to either the intervention group (‘Digital solution for Salutogenic Brain health’ programme) or the waitlist control group. Cognitive tests, clinical measures, questionnaires and interviews will be used to evaluate outcome measures. The Reach, Effectiveness, Adoption, Implementation, Maintenance framework and the Capability, Opportunity, Motivation and Behaviour model will be employed to evaluate clinical effectiveness and implementation strategies. Ethics and dissemination This study has been reviewed and approved by the National Healthcare Group Domain Specific Review Board (NHG DSRB) in Singapore (NHG DSRB Reference Number: 2023/00620 (25 June 2024)). Data will be analysed by study team members and findings will be published in peer-reviewed journals. Trial registration number NCT06582316.
Identifiability Efforts: Exchangeability and positivity are implied due to randomisation.
Pitfalls: The authors do not state intend to measure adherence. Handling of missingness and investigation of reasons for missingness is not specified (except reasons for no-enrolment despite eligibility). Ways of dealing with informative drop-out are not specified.
Authors: Huiming Liu, Guanjie Chen, Jinghua Li, Chun Hao, Bin Zhang, Yuanhan Bai, Liangchen Song, Chang Chen, Haiyan Xie, Tiebang Liu, Eric D Caine, Fengsu Hou
Year: 2021
Sample size: 312
Introduction The postdischarge suicide risk among psychiatric patients is significantly higher than it is among patients with other diseases and general population. The brief contact interventions (BCIs) are recommended to decrease suicide risk in areas with limited mental health service resources like China. This study aims to develop a postdischarge suicide intervention strategy based on BCIs and evaluate its implementability under the implementation outcome framework. Methods and analysis This study will invite psychiatric patients and family members, clinical and community mental health service providers as the community team to develop a postdischarge suicide intervention strategy. The study will recruit 312 patients with psychotic symptoms and 312 patients with major depressive disorder discharged from Shenzhen Kangning Hospital (SKH) in a Sequential Multiple Assignment Randomised Trial. Participants will be initially randomised into two intervention groups to receive BCIs monthly and weekly, and they will be rerandomised into three intervention groups to receive BCIs monthly, biweekly and weekly at 3 months after discharge according to the change of their suicide risk. Follow-ups are scheduled at 1, 3, 6 and 12 months after discharge. With the intention-to-treat approach, generalised estimating equation and survival analysis will be applied. This study will also collect qualitative and quantitative information on implementation and service outcomes from the community team. Ethics/dissemination This study has received ethical approval from the Ethics Committee Review Board of SKH. All participants will provide written informed consent prior to enrolment. The findings of the study will be disseminated through peer-reviewed scientific journals, conference presentations. A project report will be submitted to the National Natural Science Foundation of China as the concluding report of this funded project, and to the mental health authorities in the Shenzhen to refine and apply evidence-based and pragmatic interventions into health systems for postdischarge suicide prevention. Trial registration number NCT04907669.
Identifiability Efforts: Exchangeability and positivity is implied due to the use of randomisation. In addition, generalised estimating equations will be used to adjust for confounding. The authors intend to re-randomise non-responders, thus using a DTR to handle ICE relating to treatment-response.
Pitfalls: Since participants will be recruited from the same hospital there is potential for interference among patients that are in contact with each other (despite them being discharged at the time of the intervention). The authors intend to perform survival analysis but did not report plans on handling informative censoring. They do intend to analyse adherence rates. The authors do not state to investigate reasons for missingness, but do state handling by multiple imputation assuming MAR.
Authors: Chelsey R Schlechter, Guilherme Del Fiol, Dusti R Jones, Brian Orleans, Bryan Gibson, Inbal Nahum-Shani, Ellen Maxfield, Amy Locke, Ryan Cornia, Richard Bradshaw, Jennifer Wirth, Shanna J Jaggers, Cho Y Lam, David W Wetter
Year: 2023
Sample size: 200
Introduction Over 40% of US adults meet criteria for obesity, a major risk factor for chronic disease. Obesity disproportionately impacts populations that have been historically marginalised (eg, low socioeconomic status, rural, some racial/ethnic minority groups). Evidence-based interventions (EBIs) for weight management exist but reach less than 3% of eligible individuals. The aims of this pilot randomised controlled trial are to evaluate feasibility and acceptability of dissemination strategies designed to increase reach of EBIs for weight management. Methods and analysis This study is a two-phase, Sequential Multiple Assignment Randomized Trial, conducted with 200 Medicaid patients. In phase 1, patients will be individually randomised to single text message (TM1) or multiple text messages (TM+). Phase 2 is based on treatment response. Patients who enrol in the EBI within 12 weeks of exposure to phase 1 (ie, responders) receive no further interventions. Patients in TM1 who do not enrol in the EBI within 12 weeks of exposure (ie, TM1 non-responders) will be randomised to either TM1-Continued (ie, no further TM) or TM1 & MAPS (ie, no further TM, up to 2 Motivation And Problem Solving (MAPS) navigation calls) over the next 12 weeks. Patients in TM+ who do not enrol in the EBI (ie, TM+ non-responders) will be randomised to either TM+Continued (ie, monthly text messages) or TM+ & MAPS (ie, monthly text messages, plus up to 2 MAPS calls) over the next 12 weeks. Descriptive statistics will be used to characterise feasibility (eg, proportion of patients eligible, contacted and enrolled in the trial) and acceptability (eg, participant opt-out, participant engagement with dissemination strategies, EBI reach (ie, the proportion of participants who enrol in EBI), adherence, effectiveness). Ethics and dissemination Study protocol was approved by the University of Utah Institutional Review Board (#00139694). Results will be disseminated through study partners and peer-reviewed publications. Trial registration number clinicaltrials.gov; NCT05666323.
Identifiability Efforts: Exchangeability and positivity are implied due to the use of stratified sampling. ICE relating to non-response are handled by being a tailouring variable in the SMART. In fact, since the endpoint is defined by missingness, any ICE is effectively handled (composite strategy).
Pitfalls: The trialists do not intend to investigate reasons for missingness. Due to missingness representing the outcome of interest, most pitfalls relating to missingness are not applicable.
Authors: Leah L. Zullig, Mohammad Shahsahebi, Benjamin Neely, Terry Hyslop, Renee A. V. Avecilla, Brittany M. Griffin, Kacey Clayton-Stiglbauer, Theresa Coles, Lynda Owen, Bryce B. Reeve, Kevin Shah, Rebecca A. Shelby, Linda Sutton, Michaela A. Dinan, S. Yousuf Zafar, Nishant P. Shah, Susan Dent, Kevin C. Oeffinger
Year: 2021
Sample size: 800
Background As treatments for cancer have improved, more people are surviving cancer. However, compared to people without a history of cancer, cancer survivors are more likely to die of cardiovascular disease (CVD). Increased risk for CVD-related mortality among cancer survivors is partially due to lack of medication adherence and problems that exist in care coordination between cancer specialists, primary care physicians, and cardiologists. Methods/Design The Onco-primary care networking to support TEAM-based care (ONE TEAM) study is an 18-month cluster-randomized controlled trial with clustering at the primary care clinic level. ONE TEAM compares the provision of the iGuide intervention to patients and primary care providers versus an education-only control. For phase 1, at the patient level, the intervention includes video vignettes and a live webinar; provider-level interventions include electronic health records-based communication and case-based webinars. Participants will be enrolled from across North Carolina one of their first visits with a cancer specialist (e.g., surgeon, radiation or medical oncologist). We use a sequential multiple assignment randomized trial (SMART) design., Outcomes (measured at the patient level) will include Healthcare Effectiveness Data and Information Set (HEDIS) quality measures of management of three CVD comorbidities using laboratory testing (glycated hemoglobin [A1c], lipid profile) and blood pressure measurements; (2) medication adherence assessed pharmacy refill data using Proportion of Days Covered (PDC); and (3) patient-provider communication (Patient-Centered Communication in Cancer Care, PCC-Ca-36)., Primary care clinics in the intervention arm will be considered non-responders if 90% or more of their participating patients do not meet the modified HEDIS quality metrics at the 6-month measurement, assessed once the first enrollee from each practice reaches the 12-month mark. Non-responders will be re-randomized to either continue to receive the iGuide 1 intervention, or to receive the iGuide 2 intervention, which includes tailored videos for participants and specialist consults with primary care providers. Discussion As the population of cancer survivors grows, ONE TEAM will contribute to closing the CVD outcomes gap among cancer survivors by optimizing and integrating cancer care and primary care teams. ONE TEAM is designed so that it will be possible for others to emulate and implement at scale. Trial registration This study (NCT04258813) was registered in clinicaltrals.gov on February 6, 2020.
Identifiability Efforts: Exchangeability and positivity are implied by randomisation. SUTVA is implicitly addressed by the authors who employ cluster-level randomisation (clinic-level) to address possible interference (contamination). Clinics with a high-rate of non-responders will be re-randomised to interventions tailoured to this, thereby employing a DTR strategy to address treatment non-response.
Pitfalls: The authors do intend to measure adherence. The authors do not address potential informative censoring. Reasons for non-participation are collected, but the authors do not state intent to collect data on reasons for missingness, or handling of missing data generally.