Maximizing accurate detection of divergence from normative expectation in behavioral intervention outcome assessment / Katie HUBA ; Allison R. FRAZIER ; Rebecca A. WOMACK ; Eric A. YOUNGSTROM ; Lacey CHETCUTI ; Antonio Y. HARDAN ; Mirko ULJAREVIC in Research in Autism, 126 (August 2025)  

Public ISBD [article]  inResearch in Autism > 126 (August 2025) . - p.202646 Titre : Maximizing accurate detection of divergence from normative expectation in behavioral intervention outcome assessment Type de document : Texte imprimé et/ou numérique Auteurs : Katie HUBA, Auteur ; Allison R. FRAZIER, Auteur ; Rebecca A. WOMACK, Auteur ; Eric A. YOUNGSTROM, Auteur ; Lacey CHETCUTI, Auteur ; Antonio Y. HARDAN, Auteur ; Mirko ULJAREVIC, Auteur Article en page(s) : p.202646 Langues : Anglais (eng) Mots-clés : Norms  Behavioral intervention  Outcomes  Progress monitoring  Generalized additive models Index. décimale : PER Périodiques Résumé : Behavioral interventions have shown substantial positive effects at the group level in improving the developmental trajectory of individuals with autism spectrum disorder (ASD), including a wide range of benefits from symptom reductions to skill development. However, there remain pronounced individual differences in the response to interventions and substantial practice variability in the choice and implementation of outcome assessments to evaluate progress for individual cases. Unfortunately, legacy outcome assessments were not specifically designed for the behavioral intervention context or for use with individuals with ASD. Furthermore, legacy instruments have been normed using traditional approaches that are often very inefficient and have limited sensitivity to divergence from neurotypical expectation. Recently, new measures, specifically designed for ASD and related neurodevelopmental conditions, have been developed and revised for use as behavioral intervention outcome assessments. To maximize the value of these measures, the present study aimed to identify optimal norming methods by comparing five distinct continuous norming models. Results indicated that more complex models that include estimation of non-linear age trends fit better and appear to provide more accurate identification of deviation from normative expectation, especially at younger ages where normative data is dense. For some symptom and skill domains, inclusion of sex-specific age-trends was necessary for best fit and most accurate performance. These findings support the use of continuous norming methods using non-linear modeling of developmental trends in the norming of outcome measures for behavioral intervention. Behavior intervention outcome assessments would benefit from implementing these norming approaches to improve the ability to detect deviation from neurotypical symptom and skill levels. En ligne : https://doi.org/10.1016/j.reia.2025.202646 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=565 [article] Maximizing accurate detection of divergence from normative expectation in behavioral intervention outcome assessment [Texte imprimé et/ou numérique] / Katie HUBA, Auteur ; Allison R. FRAZIER, Auteur ; Rebecca A. WOMACK, Auteur ; Eric A. YOUNGSTROM, Auteur ; Lacey CHETCUTI, Auteur ; Antonio Y. HARDAN, Auteur ; Mirko ULJAREVIC, Auteur . - p.202646. Langues : Anglais (eng) in Research in Autism > 126 (August 2025) . - p.202646 Mots-clés : Norms  Behavioral intervention  Outcomes  Progress monitoring  Generalized additive models Index. décimale : PER Périodiques Résumé : Behavioral interventions have shown substantial positive effects at the group level in improving the developmental trajectory of individuals with autism spectrum disorder (ASD), including a wide range of benefits from symptom reductions to skill development. However, there remain pronounced individual differences in the response to interventions and substantial practice variability in the choice and implementation of outcome assessments to evaluate progress for individual cases. Unfortunately, legacy outcome assessments were not specifically designed for the behavioral intervention context or for use with individuals with ASD. Furthermore, legacy instruments have been normed using traditional approaches that are often very inefficient and have limited sensitivity to divergence from neurotypical expectation. Recently, new measures, specifically designed for ASD and related neurodevelopmental conditions, have been developed and revised for use as behavioral intervention outcome assessments. To maximize the value of these measures, the present study aimed to identify optimal norming methods by comparing five distinct continuous norming models. Results indicated that more complex models that include estimation of non-linear age trends fit better and appear to provide more accurate identification of deviation from normative expectation, especially at younger ages where normative data is dense. For some symptom and skill domains, inclusion of sex-specific age-trends was necessary for best fit and most accurate performance. These findings support the use of continuous norming methods using non-linear modeling of developmental trends in the norming of outcome measures for behavioral intervention. Behavior intervention outcome assessments would benefit from implementing these norming approaches to improve the ability to detect deviation from neurotypical symptom and skill levels. En ligne : https://doi.org/10.1016/j.reia.2025.202646 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=565

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