Predicting early intervention outcomes in autism via individual participant data mega-analysis
Veronica Mandelli1, Elena Maria Busuoli1, Michel Godel2
1Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy.
Molecular Autism
|August 6, 2026
Summary
Early autism intervention is more effective when started younger and with higher developmental quotients. Individual participant data meta-analysis reveals key predictors for tailored interventions in autism spectrum disorder.
Area of Science:
- Developmental Psychology
- Clinical Psychology
- Pediatric Medicine
Background:
- Traditional meta-analyses of autism early intervention offer limited individualized insights due to reliance on summary statistics.
- Individual participant data meta-analyses (IPD-MA) can provide more personalized findings for autism early intervention.
Purpose of the Study:
- To conduct an IPD-MA on a large dataset of autistic children receiving early intervention.
- To identify predictors of intervention outcomes and compare the effectiveness of the Early Start Denver Model (ESDM) versus other approaches.
Main Methods:
- An IPD-MA was performed on 582 autistic children across 11 datasets from diverse settings and countries.
- Interventions varied in duration and intensity, with ESDM compared against treatment-as-usual/community approaches.
- Outcomes included Mullen Scales of Early Learning (MSEL), Vineland Adaptive Behavior Scales (VABS), and Autism Diagnostic Observation Schedule (ADOS) scores.
Main Results:
- Sex and intervention intensity were not significant predictors of outcome changes.
- Age at intervention start and pre-intervention developmental quotient (DQ) significantly moderated outcomes.
- Earlier intervention start improved outcomes, while higher pre-intervention DQ enhanced VABS motor and MSEL scores. ESDM showed declining ADOS trajectories, especially in high DQ individuals, unlike non-ESDM interventions.
Conclusions:
- Age at intervention start and pre-intervention DQ are critical individualized predictors for autism early intervention.
- Pre-intervention DQ can interact with intervention type, influencing early intervention response.
- Limitations include potential self-selection bias and heterogeneity in non-ESDM interventions, necessitating larger sample sizes for detailed comparisons.
