Modelling quality of life in children with intellectual disability using regression trees

Aim: To identify factors associated with quality of life (QoL) in children with intellectual disability. We aimed to identify patterns of association not observable in previous hypothesis-driven regression modelling using the same data set from a cross-sectional observational study. Method: A questionnaire was completed by 442 caregivers of children with confirmed intellectual disability and a diagnosis of autism spectrum disorder, cerebral palsy, Down syndrome, or Rett syndrome. The Quality of Life Inventory-Disability (QI-Disability) questionnaire was used to assess child QoL. Independent variables described the child's health, functional abilities, community participation, and sociodemographics. The R package rpart was used to build the regression trees. Results: The mean total QI-Disability score was 69.2 out of a maximum 100. The subgroup with the lowest QoL scores comprised children with a high degree of daytime sleepiness (n=74, mean 57.5) while the subgroup with the highest QoL scores (n=91, mean 80.3) comprised children with little daytime sleepiness who participated more frequently in community activities and displayed good eye contact while listening. Interpretation: Regression tree analysis provides insights into the relative importance of associated factors. Sleep problems and community participation were more important than functional abilities in accounting for differences in QoL.