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

This systematic review and meta-analysis aimed to estimate the overall effectiveness of 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. Mainly, randomized controlled trial studies with extractable intervention outcomes were included. A total of 41 studies (n=3,008) were synthesized using random-effects models (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 < .001), corresponding to an estimated success proportion of 62% (95% CI: 59%-65%). Heterogeneity was substantial (Q{square}(40) = 238.78, p < .001; I{superscript 2} = 82.45%; {tau}{superscript 2} = 0.069, 95% CI: 0.028-0.137; {tau} = 0.262), with H{superscript 2} = 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, 95% CrI: 0.592-0.646), with {tau} = 0.273 and I{superscript 2} {approx} 82.5%, and MCMC diagnostics showed stable convergence (R-hat {approx} 1.00). Publication bias analyses indicated significant funnel plot asymmetry (Egger-type regression: z = 3.429, p < .001; weighted regression: t = 9.573, p < .001), while rank correlation was non-significant ({tau} = -0.178, p = .103). Trim-and-fill analysis imputed 10 studies, reducing the pooled effect to 0.374 (95% CI: 0.258-0.491; {tau} = 0.338), though the effect remained significant (p < .001). Sensitivity analyses excluding influential studies yielded a stable effect (0.505, 95% CI: 0.401-0.609), with persistent heterogeneity (I{superscript 2} = 75.49%; Q{square}(38) = 190.21, p < .001; {tau}{superscript 2} = 0.043). Subgroup analyses showed highest effects for digital/technology-based interventions (0.672; 67%; I{superscript 2} = 0%), followed by nutritional (0.635; 64%; I{superscript 2} = 73.81%), behavioral (0.630; 63%; I{superscript 2} = 74.78%), and pharmacological (0.627; 63%; I{superscript 2} = 0%) interventions, while physical/occupational therapies showed lower effects (0.523; 52%; I{superscript 2} = 63.35%) and combined interventions showed borderline effects (0.593; 59%; I{superscript 2} = 19.96%); subgroup differences were significant (Q{square}5) = 22.63, p < .001). Regional effects were similar and non-significant (Q{square}(2) = 0.73, p = .694): North America (0.619; I{superscript 2} = 84.21%), Europe (0.626; I{superscript 2} = 62.34%), and Asia (0.659; I{superscript 2} = 0%). Age at intervention onset did not significantly moderate effects (Q{square}(5) = 0.98, p = .964), although variability was observed across children, adolescents, adults, and toddlers. Meta-regression identified significant moderators including intervention context (Q{square}= 18.159, p = .020), outcome domain (Q{square}= 19.588, p = .003), age at start (Q{square}= 17.795, p = .003), and intervention category (Q{square}= 31.714, p < .001), while follow-up duration and intervention duration were not significant. Bayesian subgroup analyses confirmed robustness, with strongest evidence for pharmacological (BF [-&gt;] {infty}), behavioral (BF {approx} 832.50), and digital interventions (BF {approx} 30.92). In conclusion, ASD interventions demonstrate a moderate and statistically significant overall effect ([~]0.50-0.62), with substantial heterogeneity driven primarily by intervention type, context, and participant characteristics, and findings were consistent across frequentist, Bayesian, and sensitivity analyses, supporting robust but context-dependent effectiveness.