Georgiades Stelios, Boyle Michael, Szatmari Peter, Hanna Steven, Duku Eric, Zwaigenbaum Lonnie, Bryson Susan, Fombonne Eric, Volden Joanne, Mirenda Pat, Smith Isabel, Roberts Wendy, Vaillancourt Tracy, Waddell Charlotte, Bennett Teresa, Elsabbagh Mayada, Thompson Ann
Offord Centre for Child Studies, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4L8, Canada,
J Autism Dev Disord. 2014 Dec;44(12):3045-55. doi: 10.1007/s10803-014-2167-x.
The latent class structure of autism symptoms from the time of diagnosis to age 6 years was examined in a sample of 280 children with autism spectrum disorder. Factor mixture modeling was performed on 26 algorithm items from the Autism Diagnostic Interview - Revised at diagnosis (Time 1) and again at age 6 (Time 2). At Time 1, a "2-factor/3-class" model provided the best fit to the data. At Time 2, a "2-factor/2-class" model provided the best fit to the data. Longitudinal (repeated measures) analysis of variance showed that the "2-factor/3-class" model derived at the time of diagnosis allows for the identification of a subgroup of children (9 % of sample) who exhibit notable reduction in symptom severity.
在一个包含280名自闭症谱系障碍儿童的样本中，研究了从诊断时到6岁期间自闭症症状的潜在类别结构。对来自《自闭症诊断访谈修订版》的26个算法项目在诊断时（时间1）和6岁时（时间2）进行了因子混合建模。在时间1，“双因素/三类”模型对数据拟合最佳。在时间2，“双因素/两类”模型对数据拟合最佳。纵向（重复测量）方差分析表明，诊断时得出的“双因素/三类”模型能够识别出症状严重程度显著降低的儿童亚组（占样本的9%）。
