2017
DOI: 10.1007/s10803-017-3280-4
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Homogeneous Subgroups of Young Children with Autism Improve Phenotypic Characterization in the Study to Explore Early Development

Abstract: The objective of this study was to identify homogenous classes of young children with autism spectrum disorder (ASD) to improve phenotypic characterization. Children were enrolled in the Study to Explore Early Development between 2 and 5 years of age. 707 children were classified with ASD after a comprehensive evaluation with strict diagnostic algorithms. Four classes of children with ASD were identified from latent class analysis: mild language delay with cognitive rigidity, mild language and motor delay with… Show more

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Cited by 27 publications
(49 citation statements)
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References 43 publications
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“…Child phenotype was defined by a latent class analysis (LCA) described by Wiggins et al (2017) on the same sample of children defined herein. LCA is a statistical procedure that assumes that responses on observed variables can be explained by membership in unmeasured latent classes (Vermunt 2010).…”
Section: Methodsmentioning
confidence: 99%
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“…Child phenotype was defined by a latent class analysis (LCA) described by Wiggins et al (2017) on the same sample of children defined herein. LCA is a statistical procedure that assumes that responses on observed variables can be explained by membership in unmeasured latent classes (Vermunt 2010).…”
Section: Methodsmentioning
confidence: 99%
“…Individuals are classified into subgroups based on similar patterns of observed data that reflect probability of class membership. Wiggins et al (2017) performed a latent class analysis with the variables in Table 1. Results showed that a four-class model best fit the data with a high precision of classification (i.e., entropy = 0.92).…”
Section: Methodsmentioning
confidence: 99%
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