Abstract

Purpose: This meta-analysis synthesizes published studies using “treatment of underlying forms” (TUF) for sentence-level deficits in people with aphasia (PWA). The study aims were to examine group-level evidence for TUF efficacy, to characterize the effects of treatment-related variables sentence structural family and complexity; treatment dose) in relation to the Complexity Account of Treatment Efficacy (CATE) hypothesis, and to examine the effects of person-level variables (aphasia severity, sentence comprehension impairment, and time postonset of aphasia) on TUF response.Method: Data from 13 single-subject, multiple-baseline TUF studies, including 46 PWA, were analyzed. Bayesian generalized linear mixed-effects interrupted time series models were used to assess the effect of treatment-related variables on probe accuracy during baseline and treatment. The moderating influence of person-level variables on TUF response was also investigated.Results: The results provide group-level evidence for TUF efficacy demonstrating increased probe accuracy during treatment compared with baseline phases. Greater amounts of TUF were associated with larger increases in accuracy, with greater gains for treated than untreated sentences. The findings revealed generalization effects for sentences that were of the same family but less complex than treated sentences. Aphasia severity may moderate TUF response, with people with milder aphasia demonstrating greater gains compared with people with more severe aphasia. Sentence comprehension performance did not moderate TUF response. Greater time postonset of aphasia was associated with smaller improvements for treated sentences but not for untreated sentences.Conclusions: Our results provide generalizable group-level evidence of TUF efficacy. Treatment and generalization responses were consistent with the CATE hypothesis. Model results also identified person-level moderators of TUF (aphasia severity, time postonset of aphasia) and preliminary estimates of the effects of varying amounts of TUF for treated and untreated sentences. Taken together, these findings add to the TUF evidence and may guide future TUF treatment–candidate selection.Supplemental Material S1. Ranking within and across structural families.Supplemental Material S2. Posterior predictive check for Model 1.Supplemental Material S3. Posterior predictive check for Model 2.Supplemental Material S4. Posterior predictive check for Model 3.Supplemental Material S5. Posterior predictive check for Model 4.Supplemental Material S6. Posterior predictive check for Model 5.Supplemental Material S7. Posterior predictive p-values.Supplemental Material S8. Generalization patterns of all sentence types.Swiderski, A. M., Quique, Y. M., Dickey, M. W., & Hula, W. D. (2021). Treatment of underlying forms: A Bayesian meta-analysis of the effects of treatment and person-related variables on treatment response. Journal of Speech, Language, and Hearing Research. Advance online publication. https://doi.org/10.1044/2021_JSLHR-21-00131

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