Traditional longitudinal modeling approaches require normally distributed data and do not account for sample heterogeneity. Parenting stress, in particular, can be difficult to model across time without transforming the data as it is usually high for caregivers of children with ASD. This study used novel linear quantile mixed models (LQMMs) to model non-normal parent stress scores across two caregiver-mediated interventions involving toddlers with ASD. The sample included 86 caregiver-child dyads who were randomized to either a parent-only psychoeducational intervention or hands-on parent training in a naturalistic developmental intervention. Child and parent-related domains of the Parenting Stress Index (PSI) were the primary outcomes in this study. The PSI was collected at entry, 10-week exit, 3-month follow-up, and 6-month follow-up periods. Separate LQMMs were used to model five specific quantiles ( of the two PSI domains across the complete intervention timeline. These five quantiles effectively modeled the entire conditional distribution of parenting stress scores. The LQMMs indicated that child-related parenting stress decreased across all quantiles within both interventions, with no difference in the rate of parenting stress change between the intervention groups. For parent-related parenting stress, the effect of intervention depended on the group's stress level; some parents increased their perceived stress within the hands-on intervention at the 3-month follow-up. Overall, this study demonstrated that the use of LQMMs yielded additional information, beyond traditional longitudinal models, regarding the relationship between parenting stress within two caregiver-mediated intervention protocols. This study also discussed the methodological contributions and potential future applications of LQMMs. LAY SUMMARY: This study used a newer longitudinal modeling technique to examine how parenting stress changed across two caregiver-mediated interventions for toddlers with ASD. Results showed that certain parents in the JASPER condition might require additional support as they exit the study and enter into their first follow-up period. It was also determined that this new modeling technique could be a valuable tool to analyze highly variable data often present in ASD intervention studies.
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