Abstract
The PDD Behavior Inventory (PDDBI) has recently been shown, in a large multisite study, to discriminate well between autism spectrum disorder (ASD) and other groups when its scores were examined using a machine learning tool, Classification and Regression Trees (CART). Discrimination was good for toddlers, preschoolers, and school-age children; generalized across clinical diagnostic sites; and agreed well with Autism Diagnostic Observation Schedule (ADOS) classifications. Results also revealed three subtypes of ASD: minimally verbal, verbal, and atypical that differed in developmental history, behavior profiles, and biomedical findings. Seven subtypes of Not-ASD children were identified, two of which were relatively common. Three of the remaining five relatively rare Not-ASD subgroups had highly atypical profiles marked either by extreme aggressiveness or by extreme ritualistic behaviors. PDDBI profiles of these rare subgroups were not previously characterized. In this study, profiles of all CART subgroups based on parent and teacher PDDBIs are described, along with their implications for diagnosis and assessment.
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