Interim monitoring in sequential multiple assignment randomized trials

Interventions often require multiple decisions to improve outcomes for every student. Whether the decision to implement a practice, tailor an existing protocol, or change approaches, these decisions should be based on individual variables and outcomes via a sequence of treatment. To develop adaptive interventions that have sufficient evidence to support decisions, components, and sequences, they must be evaluated as they operate. The sequential multiple-assignment randomized trial is a design that experimentally assesses the efficacy of the decisions, components, and sequence of an adaptive intervention. The purpose of this article is to provide an overview of this novel methodology and describe how this design can provide meaningful information about components and sequence based on individual differences and response to maximize educational outcomes in special education.

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Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations

Statistical Methods in Medical Research ◽

10.1177/0962280217708654 ◽

2017 ◽

Vol 26 (4) ◽

pp. 1572-1589 ◽

Cited By ~ 13

Author(s):

Timothy NeCamp ◽

Amy Kilbourne ◽

Daniel Almirall

Keyword(s):

Randomized Trial ◽

Randomized Trials ◽

Treatment Regimens ◽

Patient Level ◽

Multiple Assignment ◽

Dynamic Treatment ◽

The Mean ◽

Cluster Randomized ◽

The Individual ◽

Cluster Level

Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.

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