Identifying predictive behavioral markers: A demonstration using automatically reinforced self-injurious behavior

Louis P Hagopian, Griffin W. Rooker, Gayane Yenokyan

Research output: Contribution to journalArticle

Abstract

Predictive biomarkers (PBioMs) are objective biological measures that predict response to medical treatments for diseases. The current study translates methods used in the field of precision medicine to identify PBioMs to identify parallel predictive behavioral markers (PBMs), defined as objective behavioral measures that predict response to treatment. We demonstrate the utility of this approach by examining the accuracy of two PBMs for automatically reinforced self-injurious behavior (ASIB). Results of the analysis indicated both functioned as good to excellent PBMs. We discuss the compatibility of this approach with applied behavior analysis, describe methods to identify additional PBMs, and posit that variables related to the mechanisms of problem behavior and putative mechanism of treatment action hold the most promise as potential PBMs. We discuss how this technology could guide individualized treatment selection, inform our understanding of problem behavior and mechanisms of treatment action, and help determine the conditional effectiveness of clinical procedures.

Original languageEnglish (US)
JournalJournal of Applied Behavior Analysis
DOIs
StateAccepted/In press - Jan 1 2018

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Keywords

  • Automatically reinforced self-injurious behavior
  • Conditional effectiveness
  • Conditional probability analysis
  • Precision medicine
  • Predictive behavioral markers

ASJC Scopus subject areas

  • Applied Psychology
  • Sociology and Political Science
  • Philosophy

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