Effectiveness and Types of Interventions for Autism Spectrum Disorder: A Systematic Review, Meta-Analysis, and...

This systematic review and meta-analysis aimed to estimate the overall effectiveness of autism spectrum disorder (ASD) interventions and identify sources of heterogeneity using frequentist and Bayesian approaches. A systematic search of PubMed/MEDLINE, Embase, Web of Science, and Scopus was conducted for studies published between January 1, 2004, and April 30, 2025. Primarily, randomized controlled trials with extractable intervention outcomes were included. A total of 41 studies (n = 3,008) were synthesized using random-effects models (restricted maximum-likelihood (REML)), Bayesian hierarchical modeling, meta-regression, and sensitivity analyses following PRISMA guidelines. The pooled random-effects estimate showed a significant positive effect of ASD interventions (effect size = 0.506, 95% CI: 0.392-0.619; z = 8.72, p < 0.001), corresponding to an estimated success proportion of 62% (95% CI: 59%-65%). Heterogeneity was substantial (Qₑ (40) = 238.78, p < 0.001; I² = 82.45%; τ² = 0.069, 95% CI: 0.028-0.137; τ = 0.262), with H² = 5.70 and a wide prediction interval (-0.020 to 1.031), indicating strong between-study variability. Bayesian meta-analysis confirmed a comparable effect (posterior mean = 0.619 (62%), 95% CrI: 0.592-0.646), with τ = 0.273 and I² ≈ 82.5%; Markov Chain Monte Carlo (MCMC) diagnostics indicated stable convergence (R-hat ≈ 1.00). Publication bias analyses indicated significant funnel plot asymmetry (Egger-type regression: z = 3.429, p < 0.001; weighted regression: t = 9.573, p < 0.001), while rank correlation was non-significant (τ = -0.178, p = 0.103). Trim-and-fill analysis imputed 10 studies, reducing the pooled effect to 0.374 (37%; 95% CI: 0.258-0.491; τ = 0.338), although the effect remained significant (p < 0.001). Sensitivity analyses excluding influential studies yielded a stable effect (0.505 (51%), 95% CI: 0.401-0.609), with persistent heterogeneity (I² = 75.49%; Qₑ (38) = 190.21, p < 0.001; τ² = 0.043). Subgroup analyses showed highest effects for digital/technology-based interventions (0.672 (67%); I² = 0%), followed by nutritional (0.635 (64%); I² = 73.81%), behavioral (0.630 (63%); I² = 74.78%), and pharmacological (0.627 (63%); I² = 0%) interventions, while physical/occupational therapies showed lower effects (0.523 (52%); I² = 63.35%) and combined interventions showed borderline effects (0.593 (59%); I² = 19.96%); subgroup differences were significant (Q(5) = 22.63, p < 0.001). Regional effects were similar and non-significant across North America, Europe, and Asia. Meta-regression identified significant moderators including intervention context (Qₘ = 18.159, p = 0.020), outcome domain (Qₘ = 19.588, p = 0.003), age at intervention onset (Qₘ = 17.795, p = 0.003), and intervention category (Qₘ = 31.714, p < 0.001), while follow-up and intervention duration were not significant. Bayesian subgroup analyses confirmed strongest evidence for pharmacological, behavioral, and digital interventions. Overall, ASD interventions demonstrated a moderate and statistically significant overall effect (~0.50-0.62 (50-62%)), with substantial heterogeneity driven primarily by intervention type, context, and participant characteristics. Findings were consistent across frequentist, Bayesian, and sensitivity analyses, supporting robust but context-dependent effectiveness.