Billets de Michelle Dawson

Billet publié sur Twitter/X le 02/08/2012 15:11

Twitter/X Publication avec DOI crossref Extrait cité dans le billet Lien intégré au billet Termes sur l’autisme

Liens cités structurés

1 ressource citée

Publication avec DOI Récupéré Publication crossref

Stelios Georgiades, Peter Szatmari, Michael Boyle, Steven Hanna, Eric Duku, Lonnie Zwaigenbaum, Susan Bryson, Eric Fombonne, et al. (2013). Investigating phenotypic heterogeneity in children with autism spectrum disorder: a factor mixture modeling approach. Journal of Child Psychology and Psychiatry, 54(2), 206-215. Wiley.

Date de publication
août 2012
Identifiant
10.1111/j.1469-7610.2012.02588.x
Auteurs
Stelios Georgiades, Peter Szatmari, Michael Boyle, Steven Hanna, Eric Duku, Lonnie Zwaigenbaum, Susan Bryson, Eric Fombonne, Joanne Volden, Pat Mirenda, Isabel Smith, Wendy Roberts, Tracy Vaillancourt, Charlotte Waddell, Teresa Bennett, Ann Thompson, Pathways in ASD Study Team
Source
Journal of Child Psychology and Psychiatry
Détails
54(2), 206-215
Type de référence
article
Éditeur
Wiley
Source de métadonnées
crossref

Résumé

Background: Autism spectrum disorder (ASD) is characterized by notable phenotypic heterogeneity, which is often viewed as an obstacle to the study of its etiology, diagnosis, treatment, and prognosis. On the basis of empirical evidence, instead of three binary categories, the upcoming edition of the DSM 5 will use two dimensions – social communication deficits (SCD) and fixated interests and repetitive behaviors (FIRB) – for the ASD diagnostic criteria. Building on this proposed DSM 5 model, it would be useful to consider whether empirical data on the SCD and FIRB dimensions can be used within the novel methodological framework of Factor Mixture Modeling (FMM) to stratify children with ASD into more homogeneous subgroups. Methods: The study sample consisted of 391 newly diagnosed children (mean age 38.3 months; 330 males) with ASD. To derive subgroups, data from the Autism Diagnostic Interview‐Revised indexing SCD and FIRB were used in FMM; FMM allows the examination of continuous dimensions and latent classes (i.e., categories) using both factor analysis (FA) and latent class analysis (LCA) as part of a single analytic framework. Results: Competing LCA, FA, and FMM models were fit to the data. On the basis of a set of goodness‐of‐fit criteria, a ‘two‐factor/three‐class’ factor mixture model provided the overall best fit to the data. This model describes ASD using three subgroups/classes (Class 1: 34%, Class 2: 10%, Class 3: 56% of the sample) based on differential severity gradients on the SCD and FIRB symptom dimensions. In addition to having different symptom severity levels, children from these subgroups were diagnosed at different ages and were functioning at different adaptive, language, and cognitive levels. Conclusions: Study findings suggest that the two symptom dimensions of SCD and FIRB proposed for the DSM 5 can be used in FMM to stratify children with ASD empirically into three relatively homogeneous subgroups.

Auteur·ices de l’étude dans cette référence citée