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The goal of this clinical trial is to investigate predictors of treatment outcome, and the effect of individual treatment components of Goal Management Training (GMT) for improvement of cognitive control function in people with acquired brain injury (ABI).
Primary aim: To identify demographic, clinical and cognitive predictors of treatment response in Goal Management Training after acquired brain injury (ABI)?
Secondary aims: To investigate the effects of a) extended cuing (via a smartphone) and b) a booster module?
Full description
The Global Executive Composite (GEC) score derived from BRIEF-A will be used as the primary outcome measure. A selection of other included measures will be used as secondary outcome measures. Data will be analyzed based on an intention-to-treat approach. Penalized linear regression by the elastic net approach (a combination of the Lasso and Ridge regression approaches) will be used to identify demographic, clinical and cognitive predictors of outcome at 6 months after treatment (T3), which is the primary aim of the study. For the secondary aim of investigating the differences in outcome for primary and secondary outcomes between "GMT Cuing" and "GMT Usual", and between "GMT Boost" and "GMT No Boost", linear mixed models (LMMs) will be used. Data for all time points will be included, but of primary interest are differences at T2 (immediately after treatment) for assessing the effect of cuing, and at T3 (6 months after treatment) for the effect of boosting. The LMMs can account for within-subject correlations due to repeated measurements. In addition, the investigators will perform exploratory moderation and mediation analyses across both treatment groups. For the penalized regression models, complete case analyses will be performed as long as the number of missing observations is small. Otherwise, imputation will be considered, but imputation is not straightforward for variable selection models. Linear mixed models can handle missing data for the outcome variable. Considering multiple testing linked to several secondary outcomes, p-values will be interpreted with care rather than using a formal p-value adjustment. Results will be interpreted according to the magnitude of the group difference (effect size) as well as the p-values. Data will be analyzed using IBM SPSS, STATA and R.
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116 participants in 4 patient groups
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Alexander Olsen, PhD; Janne-Birgitte Børke
Data sourced from clinicaltrials.gov
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