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01.12.2018 | Research | Ausgabe 1/2018 Open Access

Journal of Neurodevelopmental Disorders 1/2018

Classifying and characterizing the development of adaptive behavior in a naturalistic longitudinal study of young children with autism

Zeitschrift:
Journal of Neurodevelopmental Disorders > Ausgabe 1/2018
Autoren:
Cristan Farmer, Lauren Swineford, Susan E. Swedo, Audrey Thurm
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s11689-017-9222-9) contains supplementary material, which is available to authorized users.

Abstract

Background

Adaptive behavior, or the ability to function independently in ones’ environment, is a key phenotypic construct in autism spectrum disorder (ASD). Few studies of the development of adaptive behavior during preschool to school-age are available, though existing data demonstrate that the degree of ability and impairment associated with ASD, and how it manifests over time, is heterogeneous. Growth mixture models are a statistical technique that can help parse this heterogeneity in trajectories.

Methods

Data from an accelerated longitudinal natural history study (n = 105 children with ASD) were subjected to growth mixture model analysis. Children were assessed up to four times between the ages of 3 to 7.99 years.

Results

The best fitting model comprised two classes of trajectory on the Adaptive Behavior Composite score of the Vineland Adaptive Behavior Scale, Second Edition—a low and decreasing trajectory (73% of the sample) and a moderate and stable class (27%).

Conclusions

These results partially replicate the classes observed in a previous study of a similarly characterized sample, suggesting that developmental trajectory may indeed serve as a phenotype. Further, the ability to predict which trajectory a child is likely to follow will be useful in planning for clinical trials.
Zusatzmaterial
Additional file 1: Figure S1. Data coverage. Figure S2. Raw ABC scores. Table S1. Model fit indices. Table S2. Model information. Table S3. Classification quality. Table S4. Correlates of class membership. Table S5. Mplus syntax for growth mixture models. (PDF 778 kb)
11689_2017_9222_MOESM1_ESM.pdf
Literatur
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