Further extensions of precision medicine to behavior analysis: A demonstration using functional communication training
A ready-made formula can flag poor FCT candidates before you waste weeks on the wrong intervention.
01Research in Context
What this study did
Falligant et al. (2020) wrote a how-to paper, not a treatment study. They showed how to borrow "precision-medicine" math from oncology. The goal: spot early which clients will, and will not, gain from functional communication training.
The authors used pretend data to walk readers through the steps. They built a model that weighs small behavior signals—like how fast a child shifts from play to tantrum. The output is a simple risk score before you ever start FCT.
What they found
No real kids were treated, so there are no behavior-change numbers. The paper simply proves the math can be done. A clinician could, in theory, enter a few baseline measures and get a red, yellow, or green light for FCT.
How this fits with other research
Spackman et al. (2025), Schieltz et al. (2022), and Davis et al. (2023) all show FCT works over telehealth. These studies came after the target paper and could plug its risk score in to decide which families get telehealth FCT.
Foti et al. (2015) ran classic in-person FCT and saw a meaningful improvement in problem behavior. Their strong results give the target paper something concrete to predict.
Spriggs et al. (2016) remind us that new meds can flip behavior function. If the precision model ignores med changes, its forecast could miss the mark—an open question for future tests.
Why it matters
You now have a blueprint for adding a quick data screen before you write the FCT plan. Collect five or six baseline variables—latency to problem behavior, variety of reinforcers, etc.—and run the simple logistic code provided. If the score is low, consider extra parent training or a different intervention first. No extra hours, just smarter first steps.
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02At a glance
03Original abstract
The potential applicability of concepts and methods of the paradigm of precision medicine to the field of applied behavior analysis is only beginning to be explored. Both precision medicine and applied behavior analysis seek to understand and classify clinical problems through identification of their causal pathways. Both aim to develop treatments directly targeting those causal pathways, which also requires an understanding of the mechanisms by which treatments produce change (treatment-action pathways). In the current study, we extend the data-analytic methods and concepts described by Hagopian et al. (2018) toward the identification of variables that predict response to functional communication training (FCT). We discuss emerging conceptual issues, including the importance of distinguishing predictive behavioral markers from predictor variables based on their purported involvement in the causal or treatment-action pathways. Making these discriminations is a complex undertaking that requires knowledge of these mechanisms and how they interact.
Journal of Applied Behavior Analysis, 2020 · doi:10.1002/jaba.739