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

Abstract

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.

What this paper adds: A hypothesis-free regression tree analysis enables examination of multiple factors potentially influencing quality of life (QoL) in children with intellectual disability. Functional abilities were less strongly associated with QoL than sleep problems and community participation.

Figure 1

Regression tree for Quality of Life Inventory‐Disability (QI‐Disability) total score showing mean value and n (%) for each node. Boxplots show distribution of scores within each terminal node. The boxes show, from bottom to top, the 25th centile, median, and 75th centile values, and the error bars indicate the lower and upper adjacent values. The upper adjacent value is defined as the largest data point ≤75th centile +1.5 interquartile range (IQR), and the lower adjacent value is defined as the smallest data point ≥25th centile – 1.5 IQR. The outside values, which are data points more extreme than the upper and lower adjacent values, are individually plotted. DOES, Disorders of Excessive Somnolence

Figure 2

Variable importance. Indicates the variance reduction contribution of variables in generating the regression tree (before pruning) for total Quality of Life Inventory‐Disability score. x‐axis values are scaled to sum to 100. DOES, Disorders of Excessive Somnolence; DIMS, Disorders of Initiating and Maintaining Sleep

Figure 3

Regression trees for the Quality of Life Inventory‐Disability (QI‐Disability) subdomains showing mean score and n (%) for each node. DIMS, Disorders of Initiating and Maintaining Sleep; DOES, Disorders of Excessive Somnolence