Autism is currently viewed as a spectrum condition including strikingly different severity levels. IQ is consistently described as one of the primary aspects of the heterogeneity in autism. To investigate the possibility of more than one distinct subtype of autism based on IQ, both latent class analysis and taxometric methods were used to classify Mullen IQ scores in a sample of children with autism spectrum disorder (N=456). Evidence for multiple IQ-based subgroups was found using both methods. Groups differed in level of intellectual functioning and patterns of verbal versus nonverbal ability. Results support the notion of distinct subtypes of autism which differ in severity of intellectual ability, patterns of cognitive strengths and weaknesses, and severity of autism symptoms.

IntroductionThe Uniform Data Set (UDS) contains neuropsychological test scores and demographic information for participants at Alzheimer's disease centers across the United States funded by the National Institute on Aging. Mean regression analysis of neuropsychological tests has been proposed to detect cognitive decline, but the approach requires stringent assumptions.MethodsWe propose using quantile regression to directly model conditional percentiles of neuropsychological test scores. An online application allows users to easily implement the proposed method.ResultsScores from 13 different neuropsychological tests were analyzed for 5413 cognitively normal participants in the UDS. Quantile and mean regression models were fit using age, gender, and years of education. Differences between the mean and quantile regression estimates were found on the individual measures.DiscussionQuantile regression provides more robust estimates of baseline percentiles for cognitively normal adults. This can then serve as standards against which to detect individual cognitive decline.

No abstract