Experimental design and primary data analysis methods for comparing adaptive interventions.

TLDR

Adaptive interventions replace fixed approaches by allowing individualized adaptation over time, guided by decision rules to optimize long‑term effectiveness. The authors review adaptive interventions and propose the SMART design to inform construction of high‑quality adaptive interventions, comparing it with other experimental approaches. The study introduces the SMART experimental design and outlines its analytic framework for addressing primary research questions. The authors provide methods for analyzing SMART data to inform the construction of high‑quality adaptive interventions.

Abstract

In recent years, research in the area of intervention development has been shifting from the traditional fixed-intervention approach to adaptive interventions, which allow greater individualization and adaptation of intervention options (i.e., intervention type and/or dosage) over time. Adaptive interventions are operationalized via a sequence of decision rules that specify how intervention options should be adapted to an individual's characteristics and changing needs, with the general aim to optimize the long-term effectiveness of the intervention. Here, we review adaptive interventions, discussing the potential contribution of this concept to research in the behavioral and social sciences. We then propose the sequential multiple assignment randomized trial (SMART), an experimental design useful for addressing research questions that inform the construction of high-quality adaptive interventions. To clarify the SMART approach and its advantages, we compare SMART with other experimental approaches. We also provide methods for analyzing data from SMART to address primary research questions that inform the construction of a high-quality adaptive intervention.