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In recent years, there has been increased interest in the development of adaptive interventions across various domains of health and psychological research. An adaptive intervention is a protocolized sequence of individualized treatments that seeks to address the unique and changing needs of individuals as they progress through an intervention program. The sequential, multiple assignment, randomized trial (SMART) is an experimental study design that can be used to build the empirical basis for the construction of effective adaptive interventions. A SMART involves multiple stages of randomizations; each stage of randomization is designed to address scientific questions concerning the best intervention option to employ at that point in the intervention. Several adaptive interventions are embedded in a SMART by design; many SMARTs are motivated by scientific questions that concern the comparison of these embedded adaptive interventions. Until recently, analysis methods available for the comparison of adaptive interventions were limited to end-of-study outcomes. The current article provides an accessible and comprehensive tutorial to a new methodology for using repeated outcome data from SMART studies to compare adaptive interventions. We discuss how existing methods for comparing adaptive interventions in terms of end-of-study outcome data from a SMART can be extended for use with longitudinal outcome data. We also highlight the scientific utility of using longitudinal data from a SMART to compare adaptive interventions. A SMART study aiming to develop an adaptive intervention to engage alcohol- and cocaine-dependent individuals in treatment is used to demonstrate the application of this new methodology. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Factorial experimental designs have many applications in the behavioral sciences. In the context of intervention development, factorial experiments play a critical role in building and optimizing high-quality, multicomponent behavioral interventions. One challenge in implementing factorial experiments in the behavioral sciences is that individuals are often clustered in social or administrative units and may be more similar to each other than to individuals in other clusters. This means that data are dependent within clusters. Power planning resources are available for factorial experiments in which the multilevel structure of the data is due to individuals' membership in groups that existed before experimentation. However, in many cases clusters are generated in the course of the study itself. Such experiment-induced clustering (EIC) requires different data analysis models and power planning resources from those available for multilevel experimental designs in which clusters exist prior to experimentation. Despite the common occurrence of both experimental designs with EIC and factorial designs, a bridge has yet to be built between EIC and factorial designs. Therefore, resources are limited or nonexistent for planning factorial experiments that involve EIC. This article seeks to bridge this gap by extending prior models for EIC, developed for single-factor experiments, to factorial experiments involving various types of EIC. We also offer power formulas to help investigators decide whether a particular experimental design involving EIC is feasible. We demonstrate that factorial experiments can be powerful and feasible even with EIC. We discuss design considerations and directions for future research. (PsycINFO Database Record

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).

Factorial experimental designs have many potential advantages for behavioral scientists. For example, such designs may be useful in building more potent interventions by helping investigators to screen several candidate intervention components simultaneously and to decide which are likely to offer greater benefit before evaluating the intervention as a whole. However, sample size and power considerations may challenge investigators attempting to apply such designs, especially when the population of interest is multilevel (e.g., when students are nested within schools, or when employees are nested within organizations). In this article, we examine the feasibility of factorial experimental designs with multiple factors in a multilevel, clustered setting (i.e., of multilevel, multifactor experiments). We conduct Monte Carlo simulations to demonstrate how design elements-such as the number of clusters, the number of lower-level units, and the intraclass correlation-affect power. Our results suggest that multilevel, multifactor experiments are feasible for factor-screening purposes because of the economical properties of complete and fractional factorial experimental designs. We also discuss resources for sample size planning and power estimation for multilevel factorial experiments. These results are discussed from a resource management perspective, in which the goal is to choose a design that maximizes the scientific benefit using the resources available for an investigation.

Increasing interest in individualizing and adapting intervention services over time has led to the development of adaptive interventions. Adaptive interventions operationalize the individualization of a sequence of intervention options over time via the use of decision rules that input participant information and output intervention recommendations. We introduce Q-learning, which is a generalization of regression analysis to settings in which a sequence of decisions regarding intervention options or services is made. The use of Q is to indicate that this method is used to assess the relative quality of the intervention options. In particular, we use Q-learning with linear regression to estimate the optimal (i.e., most effective) sequence of decision rules. We illustrate how Q-learning can be used with data from sequential multiple assignment randomized trials (SMARTs; Murphy, 2005) to inform the construction of a more deeply tailored sequence of decision rules than those embedded in the SMART design. We also discuss the advantages of Q-learning compared to other data analysis approaches. Finally, we use the Adaptive Interventions for Children With ADHD SMART study (Center for Children and Families, University at Buffalo, State University of New York, William E. Pelham as principal investigator) for illustration.

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.