Approaches to Statistical Efficiency When Comparing the Embedded Adaptive Interventions in a SMART

Timothy Lycurgus, Amy Kilbourne and Daniel Almirall
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Timothy Lycurgus: University of Michigan
Amy Kilbourne: University of Michigan Medical School
Daniel Almirall: University of Michigan

Journal of Educational and Behavioral Statistics, 2025, vol. 50, issue 3, 420-448

Abstract: Sequential, multiple assignment randomized trials (SMARTs), which assist in the optimization of adaptive interventions, are growing in popularity in education and behavioral sciences. This is unsurprising, as adaptive interventions reflect the sequential, tailored nature of learning in a classroom or school. Nonetheless, as is true elsewhere in education research, observed effect sizes in education-based SMARTs are frequently small. As a consequence, statistical efficiency is of paramount importance in their analysis. The contributions of this manuscript are twofold. First, we provide an overview of adaptive interventions and SMART designs for researchers in education science. Second, we propose four techniques that have the potential to improve statistical efficiency in the analysis of SMARTs. We demonstrate the benefits of these techniques in SMART settings both through the analysis of a SMART designed to optimize an adaptive intervention for increasing cognitive behavioral therapy delivery in school settings and through a comprehensive simulation study. Each of the proposed techniques is easily implementable, either with over-the-counter statistical software or through R code provided in Supplemental Material.

Keywords: Statistical Efficiency; Study Design (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:50:y:2025:i:3:p:420-448

DOI: 10.3102/10769986241251419

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