Incorporating participants' welfare into sequential multiple assignment randomized trials.

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

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.

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.

BACKGROUND: Diabetes 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. METHODS: Using 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. DISCUSSION: This 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 REGISTRATION: ClinicalTrials.gov: NCT06040463. Registered on September 7, 2023.