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

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.

The consecutive controlled case series: Design, data‐analytics, and reporting methods supporting the study of generality

“…If the outcomes vary across cases, then additional methods should be employed to identify factors associated with positive and negative outcomes (see Derby et al, ; Hagopian et al, ; Iwata, Pace et al, 1994; Phillips et al, ). Nonparametric statistical analyses (e.g., conditional probability analysis; Hagopian et al, ) could enable the formal quantification of variables that are associated with different outcomes and inform the development of hypotheses that could be tested in a subsequent experimental analysis. Using such methods when reporting outcomes provides an opportunity to directly examine variations in outcomes which is necessary to the study of generality.…”

Injury characteristics across functional classes of self‐injurious behavior

“…Further replication is needed, ideally by other research groups using larger samples, including participants with Subtype 1 ASIB. However, these findings suggest that, in addition to Subtype 2 ASIB being highly resistant to treatment (Hagopian et al, ), this class of behavior is also more dangerous (i.e., produces more injuries and more severe injuries). These results also suggest that the rate of SIB in the FA test condition is less important to injury production than the relative rate of SIB across FA test and control conditions.…”

“…Rate of SIB was selected for analysis, as the number of times an individual engages in SIB could be directly related to injuries. Percentage differentiation in the FA was selected for analysis, as this dimension of responding has been shown to predict response to treatment (Hagopian, Rooker, & Yenokyan, ), and thus, may be an important variable related to injuries.…”

Classification of self‐injurious behaviour across the continuum of relative environmental–biological influence

“…This is perhaps best illustrated with the example of precision medicine, made possible with advances in genomics and other technologies that enable researchers to examine diseases at the molecular level (National Research Council, 2011). This approach recognizes that seemingly similar diseases can have subtypes when examined at a more molecular level; and has recently been applied to SIB (Hagopian, et al, in press). The traditional model of disease classification in terms of signs and symptoms is giving way to a model based on the causal mechanisms of diseases, and identification of disease subtypes, thus informing individualized care based on subtype and other individual differences.…”

“…Collectively, the findings from both studies (total n = 105 cases where treatment data were available) showed dramatic differences in the responsiveness of these classes of SIB to treatment: reinforcement alone was effective in 79.3% of cases with Subtype-1 ASIB and 93.1% of cases with socially-maintained SIB, but in only 5.0% of cases with Subtype-2 ASIB. A more recent study combining data from both studies by Hagopian and colleagues (Hagopian et al, 2015; 2017) showed classification as Subtype-1 to be a predictive behavioral marker for response to treatment using reinforcement alone (positive predictive value of 82.6%, negative predictive value of 92.6%; Hagopian, Rooker, & Yenokyan, in press)…”