Designing a pilot sequential multiple assignment randomized trial for developing an adaptive treatment strategy

Abstract

There is growing interest in how best to adapt and re-adapt treatments to individuals to maximize clinical benefit. In response, adaptive treatment strategies (ATS), which operationalize adaptive, sequential clinical decision making, have been developed. From a patient's perspective an ATS is a sequence of treatments, each individualized to the patient's evolving health status. From a clinician's perspective, an ATS is a sequence of decision rules that input the patient's current health status and output the next recommended treatment. Sequential multiple assignment randomized trials (SMART) have been developed to address the sequencing questions that arise in the development of ATSs, but SMARTs are relatively new in clinical research. This article provides an introduction to ATSs and SMART designs. This article also discusses the design of SMART pilot studies to address feasibility concerns, and to prepare investigators for a full-scale SMART. As an example, we consider an example SMART for the development of an ATS in the treatment of pediatric generalized anxiety disorders. Using the example SMART, we identify and discuss design issues unique to SMARTs that are best addressed in an external pilot study prior to the full-scale SMART. We also address the question of how many participants are needed in a SMART pilot study. A properly executed pilot study can be used to effectively address concerns about acceptability and feasibility in preparation for (that is, prior to) executing a full-scale SMART.

Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research

“…Stratifying the second-stage randomization on intermediate outcomes is not possible with up-front randomization to the embedded AIs; yet such stratification is particularly attractive when nonadherence in the first-stage (to IBT) may be prognostic of outcomes to the second-stage treatments. Other practical considerations and challenges in the design and conduct of SMART are discussed elsewhere [80].…”

“…For example, there may be concern that staff's knowledge of both initial treatment assignment and the value of the tailoring variable may lead to differential assessment (e.g., information bias) in the collection of study outcomes. As with any randomized trial, the key to avoiding this type of bias in a SMART is to make a clear distinction between the measures used for treatment (e.g., tailoring variables) versus the outcomes used for research [80]. For example, in the SMART in Fig.…”

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“…This design also has the potential to inform the delivery of more cost-efficient implementation interventions (9).…”

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“…Adaptive treatment strategies have been applied to treat depression, with the STAR * D (sequenced treatment alternatives to relieve depression) treatment (Rush et al 2004), in which patients who did not respond to less-intensive therapies were randomly assigned to more intensive treatments at higher levels; to treat schizophrenia, with the CATIE (clinical antipsychotic trials of intervention effectiveness) design (Stroup et al 2003), a three-phase study where patients were randomly assigned to new treatments at successive phases if they did not respond to earlier treatments; to treat advanced prostate cancer (Wang et al 2012) by randomizing nonfavorably-responding patients to untried chemotherapy treatments at eight-week intervals, up to four times; and many other medical settings (e.g., smoking cessation (Collins et al 2005), pediatric generalized anxiety disorders (Almirall et al 2012), and mood disorders (Lavori et al 2000;Kilbourne et al 2014)). …”