Using the PDD Behavior Inventory as a Level 2 Screener: A Classification and Regression Trees Analysis.

In order to improve discrimination accuracy between Autism Spectrum Disorder (ASD) and similar neurodevelopmental disorders, a data mining procedure, Classification and Regression Trees (CART), was used on a large multi-site sample of PDD Behavior Inventory (PDDBI) forms on children with and without...

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Publicado en:Journal of Autism & Developmental Disorders Vol. 46; no. 9; pp. 3006 - 3023
Autores principales: Cohen, Ira, Liu, Xudong, Hudson, Melissa, Gillis, Jennifer, Cavalari, Rachel, Romanczyk, Raymond, Karmel, Bernard, Gardner, Judith
Formato: research tables/charts Journal Article
Publicado: Springer Nature Sep2016
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Using the PDD Behavior Inventory as a Level 2 Screener: A Classification and Regression Trees Analysis.
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          Cohen, Ira
          Liu, Xudong
          Hudson, Melissa
          Gillis, Jennifer
          Cavalari, Rachel
          Romanczyk, Raymond
          Karmel, Bernard
          Gardner, Judith
        affil: Department of Psychology , New York State Institute for Basic Research in Developmental Disabilities , 1050 Forest Hill Road Staten Island 10314 USA
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          Autism Spectrum Disorder Classification
          Human
          Questionnaires
          Male
          Female
          Infant
          Child, Preschool
          Child
          Adolescence
          Instrument Validation
          Predictive Validity
          Sensitivity and Specificity
          Confidence Intervals
          Chi Square Test
          Descriptive Statistics
          Data Analysis Software
          Maximum Likelihood
          Funding Source
          Infant: 1-23 months
          Child, Preschool: 2-5 years
          Child: 6-12 years
          Adolescent: 13-18 years
          Male
          Female
      ab: In order to improve discrimination accuracy between Autism Spectrum Disorder (ASD) and similar neurodevelopmental disorders, a data mining procedure, Classification and Regression Trees (CART), was used on a large multi-site sample of PDD Behavior Inventory (PDDBI) forms on children with and without ASD. Discrimination accuracy exceeded 80 %, generalized to an independent validation set, and generalized across age groups and sites, and agreed well with ADOS classifications. Parent PDDBIs yielded better results than teacher PDDBIs but, when CART predictions agreed across informants, sensitivity increased. Results also revealed three subtypes of ASD: minimally verbal, verbal, and atypical; and two, relatively common subtypes of non-ASD children: social pragmatic problems and good social skills. These subgroups corresponded to differences in behavior profiles and associated bio-medical findings.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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