Literature DB >> 19207624

The structure of the Autism Diagnostic Interview-Revised: diagnostic and phenotypic implications.

Anne V Snow1, Luc Lecavalier, Carrie Houts.   

Abstract

BACKGROUND: Multivariate statistics can assist in refining the nosology and diagnosis of pervasive developmental disorders (PDD) and also contribute important information for genetic studies. The Autism Diagnostic Interview-Revised (ADI-R) is one of the most widely used assessment instruments in the field of PDD. The current study investigated its factor structure and convergence with measures of adaptive, language, and intellectual functioning.
METHODS: Analyses were conducted on 1,861 individuals with PDD between the ages of 4 and 18 years (mean = 8.3, SD = 3.2). ADI-R scores were submitted to confirmatory factor analysis (CFA) and exploratory factor analysis (EFA). Analyses were conducted according to verbal status (n = 1,329 verbal, n = 532 nonverbal) and separately for algorithm items only and for all items. ADI-R scores were correlated with scores on measures of adaptive, language, and intellectual functioning.
RESULTS: Several factor solutions were examined and compared. CFAs suggested that two- and three-factor solutions were similar, and slightly superior to a one-factor solution. EFAs and measures of internal consistency provided some support for a two-factor solution consisting of social and communication behaviors and restricted and repetitive behaviors. Measures of functioning were not associated with ADI-R domain scores in nonverbal children, but negatively correlated in verbal children.
CONCLUSIONS: Overall, data suggested that autism symptomatology can be explained statistically with a two-domain model. It also pointed to different symptoms susceptible to be helpful in linkage analyses. Implications of a two-factor model are discussed.

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Mesh:

Year:  2008        PMID: 19207624     DOI: 10.1111/j.1469-7610.2008.02018.x

Source DB:  PubMed          Journal:  J Child Psychol Psychiatry        ISSN: 0021-9630            Impact factor:   8.982


  39 in total

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3.  Examining autism spectrum disorders by biomarkers: example from the oxytocin and serotonin systems.

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4.  Novel clustering of items from the Autism Diagnostic Interview-Revised to define phenotypes within autism spectrum disorders.

Authors:  Valerie W Hu; Mara E Steinberg
Journal:  Autism Res       Date:  2009-04       Impact factor: 5.216

5.  Structural hierarchy of autism spectrum disorder symptoms: an integrative framework.

Authors:  Hyunsik Kim; Cara M Keifer; Craig Rodriguez-Seijas; Nicholas R Eaton; Matthew D Lerner; Kenneth D Gadow
Journal:  J Child Psychol Psychiatry       Date:  2017-02-14       Impact factor: 8.982

6.  Subgrouping Autism Based on Symptom Severity Leads to Differences in the Degree of Convergence Between Core Feature Domains.

Authors:  Allison Whitten; Kathryn E Unruh; Robin L Shafer; James W Bodfish
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7.  A genome-wide association study of autism using the Simons Simplex Collection: Does reducing phenotypic heterogeneity in autism increase genetic homogeneity?

Authors:  Pauline Chaste; Lambertus Klei; Stephan J Sanders; Vanessa Hus; Michael T Murtha; Jennifer K Lowe; A Jeremy Willsey; Daniel Moreno-De-Luca; Timothy W Yu; Eric Fombonne; Daniel Geschwind; Dorothy E Grice; David H Ledbetter; Shrikant M Mane; Donna M Martin; Eric M Morrow; Christopher A Walsh; James S Sutcliffe; Christa Lese Martin; Arthur L Beaudet; Catherine Lord; Matthew W State; Edwin H Cook; Bernie Devlin
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8.  Model invariance across genders of the Broad Autism Phenotype Questionnaire.

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Review 9.  Diagnosing autism in neurobiological research studies.

Authors:  Rebecca M Jones; Catherine Lord
Journal:  Behav Brain Res       Date:  2012-11-12       Impact factor: 3.332

10.  Classifying Autism Spectrum Disorders by ADI-R: Subtypes or Severity Gradient?

Authors:  Hannah Cholemkery; Juliane Medda; Thomas Lempp; Christine M Freitag
Journal:  J Autism Dev Disord       Date:  2016-07
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