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Optimal allocation to treatments in a sequential multiple assignment randomized trial.

ABSTRACT: One of the main questions in the design of a trial is how many subjects should be assigned to each treatment condition. Previous research has shown that equal randomization is not necessarily the best choice. We study the optimal allocation for a novel trial design, the sequential multiple assignment randomized trial, where subjects receive a sequence of treatments across various stages. A subject's randomization probabilities to treatments in the next stage depend on whether he or she responded to treatment in the current stage. We consider a prototypical sequential multiple assignment randomized trial design with two stages. Within such a design, many pairwise comparisons of treatment sequences can be made, and a multiple-objective optimal design strategy is proposed to consider all such comparisons simultaneously. The optimal design is sought under either a fixed total sample size or a fixed budget. A Shiny App is made available to find the optimal allocations and to evaluate the efficiency of competing designs. As the optimal design depends on the response rates to first-stage treatments, maximin optimal design methodology is used to find robust optimal designs. The proposed methodology is illustrated using a sequential multiple assignment randomized trial example on weight loss management.

SUBMITTER: Morciano A

PROVIDER: S-EPMC8649474 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Baseline data of a sequential multiple assignment randomized trial (STEP study).

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| S-EPMC9795376 | biostudies-literature

A multiple imputation strategy for sequential multiple assignment randomized trials.

Project description:Sequential multiple assignment randomized trials (SMARTs) are increasingly being used to inform clinical and intervention science. In a SMART, each patient is repeatedly randomized over time. Each randomization occurs at a critical decision point in the treatment course. These critical decision points often correspond to milestones in the disease process or other changes in a patient's health status. Thus, the timing and number of randomizations may vary across patients and depend on evolving patient-specific information. This presents unique challenges when analyzing data from a SMART in the presence of missing data. This paper presents the first comprehensive discussion of missing data issues typical of SMART studies: we describe five specific challenges and propose a flexible imputation strategy to facilitate valid statistical estimation and inference using incomplete data from a SMART. To illustrate these contributions, we consider data from the Clinical Antipsychotic Trial of Intervention and Effectiveness, one of the most well-known SMARTs to date.

| S-EPMC4184954 | biostudies-literature

Estimating the optimal dynamic antipsychotic treatment regime: Evidence from the sequential multiple assignment randomized CATIE Schizophrenia Study.

Project description:Treatment of schizophrenia is notoriously difficult and typically requires personalized adaption of treatment due to lack of efficacy of treatment, poor adherence, or intolerable side effects. The Clinical Antipsychotic Trials in Intervention Effectiveness (CATIE) Schizophrenia Study is a sequential multiple assignment randomized trial comparing the typical antipsychotic medication, perphenazine, to several newer atypical antipsychotics. This paper describes the marginal structural modeling method for estimating optimal dynamic treatment regimes and applies the approach to the CATIE Schizophrenia Study. Missing data and valid estimation of confidence intervals are also addressed.

| S-EPMC3475611 | biostudies-literature

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

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| S-EPMC8481760 | biostudies-literature

Communication interventions for minimally verbal children with autism: a sequential multiple assignment randomized trial.

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| S-EPMC4030683 | biostudies-literature

Sequential Multiple Assignment Randomized Trial (SMART) to Construct Weight Loss Interventions for African American Adolescents.

Project description:The purpose of this study was to develop an adaptive behavioral treatment for African American adolescents with obesity. In a sequential multiple assignment randomized trial, 181 youth ages 12-16 years with primary obesity and their caregiver were first randomized to 3 months of home-based versus office-based delivery of motivational interviewing plus skills building. After 3 months, nonresponders to first phase treatment were rerandomized to continued home-based skills or contingency management. Primary outcome was percent overweight and hypothesized moderators were adolescent executive functioning and depression. There were no significant differences in primary outcome between home-based or office-based delivery or between continued home-based skills or contingency management for nonresponders to first-phase treatment. However, families receiving home-based treatment initially attended significantly more sessions in both phases of the trial, and families receiving contingency management attended more sessions in the second phase. Overall, participants demonstrated decreases in percent overweight over the course of the trial (3%), and adolescent executive functioning moderated this effect such that those with higher functioning lost more weight. More potent behavioral treatments to address the obesity epidemic are necessary, targeting new areas such as executive functioning. Delivering treatment in the home with contingency management may increase session attendance for this population.

| S-EPMC4658314 | biostudies-literature

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SMART: Study protocol for a sequential multiple assignment randomized controlled trial to optimize weight loss management.

Project description:BACKGROUND:Stepped care is a rational resource allocation approach to reduce population obesity. Evidence is lacking to guide decisions on use of low cost treatment components such as mobile health (mHealth) tools without compromising weight loss of those needing more expensive traditional treatment components (e.g., coaching, meal replacement). A sequential multiple assignment randomization trial (SMART) will be conducted to inform the development of an empirically based stepped care intervention that incorporates mHealth and traditional treatment components. OBJECTIVE:The primary aim tests the non-inferiority of app alone, compared to app plus coaching, as first line obesity treatment, measured by weight change from baseline to 6?months. Secondary aims are to identify the best tactic to address early treatment non-response and the optimal treatment sequence for resource efficient weight loss. STUDY DESIGN:Four hundred participants, 18-60?years old with Body Mass Index between 27 and 45?kg/m2 will be randomized to receive a weight loss smartphone app (APP) or the app plus weekly coaching (APP + C) for a 12?week period. Those achieving <0.5?lb. weight loss on average per week, assessed by wireless scale at 2, 4, and 8?weeks, will be classified as non-responders and re-randomized once to step-up modestly (adding another mHealth component) or vigorously (adding mHealth and traditional treatment components) for the remaining treatment period. Weight will be assessed in person at baseline, 3, 6, and 12?months. SIGNIFICANCE:Results will inform construction of an obesity treatment algorithm that balances weight loss outcomes with resource consumption.

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Noninferiority and equivalence tests in sequential, multiple assignment, randomized trials (SMARTs).

Project description:Adaptive interventions (AIs) are increasingly popular in the behavioral sciences. An AI is a sequence of decision rules that specify for whom and under what conditions different intervention options should be offered, in order to address the changing needs of individuals as they progress over time. The sequential, multiple assignment, randomized trial (SMART) is a novel trial design that was developed to aid in empirically constructing effective AIs. The sequential randomizations in a SMART often yield multiple AIs that are embedded in the trial by design. Many SMARTs are motivated by scientific questions pertaining to the comparison of such embedded AIs. Existing data analytic methods and sample size planning resources for SMARTs are suitable only for superiority testing, namely for testing whether one embedded AI yields better primary outcomes on average than another. This calls for noninferiority/equivalence testing methods, because AIs are often motivated by the need to deliver support/care in a less costly or less burdensome manner, while still yielding benefits that are equivalent or noninferior to those produced by a more costly/burdensome standard of care. Here, we develop data-analytic methods and sample-size formulas for SMARTs testing the noninferiority or equivalence of one AI over another. Sample size and power considerations are discussed with supporting simulations, and online resources for sample size planning are provided. A simulated data analysis shows how to test noninferiority and equivalence hypotheses with SMART data. For illustration, we use an example from a SMART in the area of health psychology aiming to develop an AI for promoting weight loss among overweight/obese adults. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

| S-EPMC7061067 | biostudies-literature

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