Treatment of Underlying Forms: A Bayesian Meta-Analysis of the Effects of Treatment and Person-Related Variables on Treatment Response

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 https://doi.org/10.23641/asha.16828630

Novel participant-level meta-analytic evidence for AbSANT efficacy

“…The purpose of this meta-analysis was to combine all available AbSANT data to answer questions related to the efficacy (RQ1), specificity (RQ2), and predictors of positive outcomes (RQ3) of this therapy. The current analytical approach of quantifying changes over the course of AbSANT via mixed-effect models, rather than quantifying pre-to-post changes via a single effect-size estimate per participant, has already been used in one published meta-analysis: Swiderski et al ( 34 ). This approach has the advantage of not only increasing the statistical power of the analyses but enabling fine-grained examination of the unfolding treatment response: for example, examining which treatment conditions (abstract vs. concrete training) result in larger, faster-emerging, or better-retained training gains.…”

“…Data from every category-generation probe for every individual were extracted by the third author and analyzed using ITS regression models ( 31 , 34 , 35 ). These weekly probe data were coded for whether they were part of the baseline (A 1 ), training (B), or withdrawal (A 2 ) phase for the purpose of ITS analysis (see below for details of the coding scheme used).…”

“…ITS models were developed to model the timecourse of intervention effects in single-subject controlled experimental designs in the education literature ( 31 ). They have recently been used successfully in both multiple-case single-subject, multiple-baseline aphasia treatment studies ( 32 , 33 ) and meta-analyses of such studies ( 34 ). ITS models use generalized linear mixed-effect regression methods to model changes in a treated behavior (for AbSANT, success of word retrieval for target concrete or abstract words) over three key treatment phases: during the baseline phase ( baseline slope ), at the initiation of treatment ( level change : the magnitude of change at the baseline-treatment phase boundary), and across the treatment phase ( slope change : change in treatment-phase slope compared to the baseline phase).…”

“…The structure of mixed-effect ITS models also enables them to examine the effect of both treatment-related variables (such as training-phase type) and person-related variables (such as semantic processing ability) on AbSANT-related changes in performance: both types of variables can serve as predictors (fixed effects) of performance in each weekly probe, while taking into account other sources of variability (random effects, such as stimulus category or persons). See Braun and Kiran ( 38 ) for an example of how mixed-effect models can be used to examine the effects of both person- and treatment-related (e.g., stimulus complexity) variables on naming treatment response, and see Swiderski et al ( 34 ) for an example of how mixed-effect ITS models may be used to examine the simultaneous effects of treatment-related and person-specific variables on sentence production treatment response. This is a particularly valuable feature of this meta-analysis.…”

Targeted neurorehabilitation strategies in post-stroke aphasia

“…A recent meta-analysis with aggregated data from 46 PWA from 13 studies reported significant generalization of treatment of underlying forms (TUF) to less complex sentence structures from more complex, treated sentences. Consistent with CATE, generalization was limited to sentences that belonged to the same structural family as the treated sentences (Swiderski et al, 2021). While evidence related to CATE is compelling, more research is needed to characterize the impact of variables such as type of treatment, frequency of treatment delivery, individual variability in impairment profiles, strokerelated factors and demographics, and how these may interact with complexity (Thompson, 2007).…”