SMART Thinking: a Review of Recent Developments in Sequential Multiple Assignment Randomized Trials

Nurse-led sequential multiple assignment randomized trial of nudging intervention for early antiretroviral therapy initiation among patients with HIV/AIDS: Implementation study protocol

AIMS

In China, more than 30% of patients have not initiated treatment within 30 days of HIV diagnosis. Delayed initiation has a detrimental influence on disease outcomes and increases HIV transmission. The study aims to evaluate the effectiveness of a nurse-led antiretroviral therapy initiation nudging intervention for people newly diagnosed with HIV in China to find the optimal intervention implementation strategy.

METHODS

A Hybrid Type II sequential multiple assignment randomized trial will be conducted at four Centers for Disease Control and Prevention in Hunan, China. This study will recruit 447 people newly diagnosed with HIV aged ≥18 years and randomly assign them into two intervention groups and one control group. On top of the regular counselling services and referrals, intervention groups will receive a 4-week, 2-phase intervention based on the dual-system theory and the nudge theory. The control group will follow the currently recommended referral procedures. The primary outcomes are whether treatment is initiated, as well as the length of time it takes. The study outcomes will be measured at the baseline, day 15, day 30, week 12, week 24 and week 48. Generalized estimating equations and survival analysis will be used to compare effectiveness and explore factors associated with antiretroviral therapy initiation. Both qualitative and quantitative information will be collected to assess implementation outcomes.

DISCUSSION

Existing strategies mostly target institutional-level factors, with little consideration given to patients' decision-making. To close this gap, we aim to develop an effective theory-driven nudging strategy to improve early ART initiation.

IMPACT

This nurse-led study will help to prevent delayed initiation by employing implementation science strategies for people newly diagnosed with HIV. This study contributes to the United Nations' objective of ending the AIDS pandemic by 2030.

TRIAL REGISTRATION

Chinese Clinical Trial Registry ChiCTR2300070140. The trial was prospectively registered before the first participant was recruited.

PATIENT AND PUBLIC INVOLVEMENT

The nudging intervention was finalized through the Nominal Group Technique where we invited five experts in the related field and five people living with HIV to participate.

Optimal Design of Clinical Trials Involving Persons with Disorders of Consciousness

BACKGROUND

Limited data exist regarding the optimal clinical trial design for studies involving persons with disorders of consciousness (DoC), and only a few therapies have been tested in high-quality clinical trials. To address this, the Curing Coma Campaign Clinical Trial Working Group performed a gap analysis on the current state of clinical trials in DoC to identify the optimal clinical design for studies involving persons with DoC.

METHODS

The Curing Coma Campaign Clinical Trial Working Group was divided into three subgroups to (1) review clinical trials involving persons with DoC, (2) identify unique challenges in the design of clinical trials involving persons with DoC, and (3) recommend optimal clinical trial designs for DoC.

RESULTS

There were 3055 studies screened, and 66 were included in this review. Several knowledge gaps and unique challenges were identified. There is a lack of high-quality clinical trials, and most data regarding patients with DoC are based on observational studies focusing on patients with traumatic brain injury and cardiac arrest. There is a lack of a structured long-term outcome assessment with significant heterogeneity in the methodology, definitions of outcomes, and conduct of studies, especially for long-term follow-up. Another major barrier to conducting clinical trials is the lack of resources, especially in low-income countries. Based on the available data, we recommend incorporating trial designs that use master protocols, sequential multiple assessment randomized trials, and comparative effectiveness research. Adaptive platform trials using a multiarm, multistage approach offer substantial advantages and should make use of biomarkers to assess treatment responses to increase trial efficiency. Finally, sound infrastructure and international collaboration are essential to facilitate the conduct of trials in patients with DoC.

CONCLUSIONS

Conduct of trials in patients with DoC should make use of master protocols and adaptive design and establish international registries incorporating standardized assessment tools. This will allow the establishment of evidence-based practice recommendations and decrease variations in care.

Bayesian sample size calculations for comparing two strategies in SMART studies

In the management of most chronic conditions characterized by the lack of universally effective treatments, adaptive treatment strategies (ATSs) have grown in popularity as they offer a more individualized approach. As a result, sequential multiple assignment randomized trials (SMARTs) have gained attention as the most suitable clinical trial design to formalize the study of these strategies. While the number of SMARTs has increased in recent years, sample size and design considerations have generally been carried out in frequentist settings. However, standard frequentist formulae require assumptions on interim response rates and variance components. Misspecifying these can lead to incorrect sample size calculations and correspondingly inadequate levels of power. The Bayesian framework offers a straightforward path to alleviate some of these concerns. In this paper, we provide calculations in a Bayesian setting to allow more realistic and robust estimates that account for uncertainty in inputs through the 'two priors' approach. Additionally, compared to the standard frequentist formulae, this methodology allows us to rely on fewer assumptions, integrate pre-trial knowledge, and switch the focus from the standardized effect size to the MDD. The proposed methodology is evaluated in a thorough simulation study and is implemented to estimate the sample size for a full-scale SMART of an internet-based adaptive stress management intervention on cardiovascular disease patients using data from its pilot study conducted in two Canadian provinces.

Use of research electronic data capture (REDCap) in a sequential multiple assignment randomized trial (SMART): a practical example of automating double randomization

BACKGROUND

Adaptive interventions are often used in individualized health care to meet the unique needs of clients. Recently, more researchers have adopted the Sequential Multiple Assignment Randomized Trial (SMART), a type of research design, to build optimal adaptive interventions. SMART requires research participants to be randomized multiple times over time, depending upon their response to earlier interventions. Despite the increasing popularity of SMART designs, conducting a successful SMART study poses unique technological and logistical challenges (e.g., effectively concealing and masking allocation sequence to investigators, involved health care providers, and subjects) in addition to other challenges common to all study designs (e.g., study invitations, eligibility screening, consenting procedures, and data confidentiality protocols). Research Electronic Data Capture (REDCap) is a secure, browser-based web application widely used by researchers for data collection. REDCap offers unique features that support researchers' ability to conduct rigorous SMARTs. This manuscript provides an effective strategy for performing automatic double randomization for SMARTs using REDCap.

METHODS

Between January and March 2022, we conducted a SMART using a sample of adult (age 18 and older) New Jersey residents to optimize an adaptive intervention to increase COVID-19 testing uptake. In the current report, we discuss how we used REDCap for our SMART, which required double randomization. Further, we share our REDCap project XML file for future investigators to use when designing and conducting SMARTs.

RESULTS

We report on the randomization feature that REDCap offers and describe how the study team automated an additional randomization that was required for our SMART. An application programming interface was used to automate the double randomizations in conjunction with the randomization feature provided by REDCap.

CONCLUSIONS

REDCap offers powerful tools to facilitate the implementation of longitudinal data collection and SMARTs. Investigators can make use of this electronic data capturing system to reduce errors and bias in the implementation of their SMARTs by automating double randomization.

TRIAL REGISTRATION

The SMART study was prospectively registered at Clinicaltrials.gov; registration number: NCT04757298, date of registration: 17/02/2021.

Sequential multiple assignment randomised trial of a brief contact intervention for suicide risk management among discharged psychiatric patients: an implementation study protocol

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.

Preconception interventions to reduce the risk of alcohol-exposed pregnancies: A systematic review

BACKGROUND

The preconception period provides a unique opportunity to optimize the health of women and children. High rates of alcohol use and unintended pregnancies are common across many Western societies, and alcohol-exposed pregnancies (AEPs) are a possible unintended outcome. The aim of the current study was to evaluate preconception interventions for the prevention of AEPs.

METHODS

A systematic search of four electronic databases (PubMed, Embase, CINAHL, and PsycINFO) was undertaken for relevant peer-reviewed articles published from 1970 onward. Studies were included if they enrolled women and/or their support networks during the preconception period.

RESULTS

Nineteen studies met the inclusion criteria. The majority of studies (n = 14) evaluated CHOICES-based interventions, which incorporate motivational interviewing approaches to change alcohol and/or contraceptive behavior. The other five interventions included a range of different approaches and modes of delivery. The majority of interventions were successful in reducing AEP risk. Changes in AEP risk were more often driven by changes in contraceptive behavior, although some approaches led to changes in both alcohol and contraceptive behavior.

CONCLUSIONS

The review indicated that many interventions were efficacious at reducing AEP risk during the preconception period through preventing unplanned pregnancy. The effectiveness estimated from these clinical trials may be greater than that seen in interventions when implemented in practice where there is a lack of blinding and greater attrition of participants during follow-up. Further research investigating the real-world effectiveness of these intervention approaches implemented across a wide range of clinical settings would be beneficial.

Precision medicine: Statistical methods for estimating adaptive treatment strategies

SERIES EDITORS' NOTE

The beauty of science is that all the important things are unpredictable. Freeman Dyson In the typescript which follows, Moodie and Krakow tackle the topical issue of precision medicine and statistical methods for estimating adaptive treatment strategies. This may be the most difficult typescript in our series so far for non-statisticians to understand. It even has equations! But please bear with the authors and give it a chance. One needs not to understand the equations to get the thrust of the strategy.Precision medicine as we discuss elsewhere, is misnamed. In statistics and mathematics precision refers to getting the same answer again and again. It does not mean getting the correct answer, the term for which is accuracy, not precision. However, precision is the current buzz word so there's no point trying to get this straight. When we think about precision we need to consider two elements, reproducibility and replicability. Reproducibility means you give me your data and computer code and I come to the same conclusion you did. Replicability is another matter. I try to replicate your experiment and hopefully reach the same conclusion. In medicine, replicability is obviously more important than reproducibility but things which cannot be reproduced are unlikely to be replicated.As the authors discuss, one can think about precision medicine as one does a family vacation. A best vacation depends on several co-variates: where you live, your prior travel experiences, advice from family and friends, online reviews, Wikitravel, cost, your travel budget, if you have kids and many other co-variates. Consequently, there is unlikely to be a best vacation for everyone. Yours might be a week at the Ritz Carlton Cancun with dinner at Careyes and ours, a week at the Pfister Hotel in Milwaukee with dinner at Mader's German Restaurant (bring simvastatin). Similarly, it is unlikely there is a best therapy of acute myeloid leukemia, a best donor, a best conditioning regimen, a best posttransplant immune suppressive regimen etc. and certainly no best combination of these co-variates for your patient.The question Moodie and Krakow tackle is how we can determine the best therapy or combination of therapies for someone receiving a haematopoietic cell transplant. Although the default answer is typically: randomized clinical trials are the gold standard, these inform us of the outcome of a cohort of subjects, not individuals. In many instances, although a new therapy may be shown to be better than an old one in a controlled randomized trial the benefit is not uniformly distributed. Some subjects in the experimental cohort may do worse with the new therapy compared with controls, others better. The question is who are the winners and losers? We cannot do a controlled randomized trial of one person. Moodie and Krakow discuss statistical tools to help us sort this out.Again, please do not be put off by the equations; forgetaboutit. The overriding message is not so complex, and important. We are always standing by on twitter @BMTStats to help. But don't confuse us with Match.com. And, by the way, Freeman Dyson was a professor at the Institute for Advanced Studies at Princeton but never got his PhD.Robert Peter Gale, Imperial College London, and Mei-Jie Zhang, Medical College of Wisconsin, Center for International Blood and Marrow Research (CIBMTR).

A SMART data analysis method for constructing adaptive treatment strategies for substance use disorders

AIMS

To demonstrate how Q-learning, a novel data analysis method, can be used with data from a sequential, multiple assignment, randomized trial (SMART) to construct empirically an adaptive treatment strategy (ATS) that is more tailored than the ATSs already embedded in a SMART.

METHOD

We use Q-learning with data from the Extending Treatment Effectiveness of Naltrexone (ExTENd) SMART (N = 250) to construct empirically an ATS employing naltrexone, behavioral intervention, and telephone disease management to reduce alcohol consumption over 24 weeks in alcohol dependent individuals.

RESULTS

Q-learning helped to identify a subset of individuals who, despite showing early signs of response to naltrexone, require additional treatment to maintain progress.

CONCLUSIONS

Q-learning can inform the development of more cost-effective, adaptive treatment strategies for treating substance use disorders.

A Scoping Review of Tailored Self-management Interventions among Adults with Mobility Impairing Neurological and Musculoskeletal Conditions

A critical public health objective is to optimize and disseminate self-management interventions for the 56.7 million adults living with chronic disabling conditions in the United States. A possible strategy to optimize the effectiveness of self-management interventions is to understand how best to tailor self-management interventions to the needs and circumstances of each participant. Thus, the purpose of this scoping review was to describe randomized controlled trials (RCTs) of tailored self-management interventions in adults with neurological and musculoskeletal conditions that characteristically result in mobility impairments. The 13 RCTs included in the scoping review typically compared tailored interventions to non-tailored interventions or usual care among adults with chronic pain, stroke, and/or arthritis. The tailored interventions were diverse in their delivery formats, dosing, behavior change techniques, and tailoring strategies. We identified 13 personal characteristics (e.g., preferences and theoretical constructs) and 4 types of assessment formats (i.e., oral history, self-report questionnaires, provider-reported assessments, and medical records) that were used to tailor the self-management interventions. It was common to tailor intervention content using self-report questionnaires that assessed personal characteristics pertaining to impairments and preferences. Content was matched to personal characteristics using clinical judgment or computer algorithms. However, few studies adequately described the decision rules for matching content. To advance the science of tailoring self-management interventions, we recommend conducting comparative effectiveness research and further developing a taxonomy to standardize descriptions of tailoring. We discuss the opportunities that are now coalescing to optimize tailored self-management. We also provide examples of how to merge concepts from the self-management literature with conceptual frameworks of tailoring from the health communication literature.