Algorithm-Driven Dosage Decisions (AD3): Optimizing Treatment for Children With Language Impairment.

Background: This study was designed to provide recommended amounts of treatment to achieve the optimal amount of language gain for children with language impairment.

Method: The authors retrospectively analyzed treatment outcomes for 233 children for delivered dose, intensity, and cumulative intensity of therapy. The steps of the analytical process they applied to arrive at algorithms for recommended amounts of treatment were (1) multilevel modeling to predict children's language gains from the 3 intensity parameters and (2) extraction of regression weights to create a recommended amount of treatment.

Results: Optimal outcomes can be identified using an equation specifying Ŷ = desired points of change (e.g., 0.6 SD units), V = child's baseline language skills, D = average number of minutes spent targeting language in a session, F = total number of sessions conducted across the year, and D × F = product of planned dose and frequency (cumulative intensity). Input of the values for Ŷ and V provides recommended amount of treatment.

Conclusions: This study constitutes the first effort to provide empirical guidance on intensity of treatment for children with language impairment. The use of algorithm-driven dosage recommendations may be more effective than clinician judgment and trial and error, although these correlational results must be confirmed with experimental methods.