Predictors of longitudinal ABA treatment outcomes for children with autism: A growth curve analysis.

Tiura et al. (2017) tracked kids with autism who got full ABA programs. They wanted to know which starting skills predicted faster growth later.

The team used growth-curve stats to map each child's learning speed across language, play, and daily living skills.

Kids who entered with stronger thinking skills climbed the learning curve faster in every area.

Starting ABA at a younger age gave kids a head start, but baseline cognition was the bigger engine for ongoing gains.

Robain et al. (2020) saw the same pattern in Swiss preschoolers: lower baseline cognition plus high intensity still produced big leaps. The two studies echo each other across continents.

Rose et al. (2020) adds a twist. In their AAC-rich program, a child's early response to pictures and symbols predicted language growth better than IQ scores. Michael's "cognition rules" finding still holds, but AAC responsivity can override it when speech devices are in play.

Song et al. (2022) narrows the lens. Only baseline expressive language, not IQ or autism severity, forecast later language gains. This looks like a contradiction, but the difference is the outcome: Michael looked at all domains; Xue-Ke looked only at spoken words.

Linstead et al. (2017) pairs nicely with Michael. Same year, same clinic. Michael tells us who learns fastest; Linstead tells us more hours and more months make the climb even steeper.

When you intake a new client, give extra thought to kids with lower cognitive scores. They can still grow, but they may need denser teaching, smaller steps, or added modalities like AAC. Pair Michael's predictor rule with Linstead's dose rule: start early, keep intensity high, and track data weekly so you can adjust the program before plateaus hit.