Abstract
ABSTRACT Background: Collation of aphasia research data across settings, countries and study designs using big data principles will support analyses across different language modalities, levels of impairment, and therapy interventions in this heterogeneous population. Big data approaches in aphasia research may support vital analyses, which are unachievable within individual trial datasets. However, we lack insight into the requirements for a systematically created database, the feasibility and challenges and potential utility of the type of data collated. Aim: To report the development, preparation and establishment of an internationally agreed aphasia after stroke research database of individual participant data (IPD) to facilitate planned aphasia research analyses. Methods: Data were collated by systematically identifying existing, eligible studies in any language (≥10 IPD, data on time since stroke, and language performance) and included sourcing from relevant aphasia research networks. We invited electronic contributions and also extracted IPD from the public domain. Data were assessed for completeness, validity of value-ranges within variables, and described according to pre-defined categories of demographic data, therapy descriptions, and language domain measurements. We cleaned, clarified, imputed and standardised relevant data in collaboration with the original study investigators. We presented participant, language, stroke, and therapy data characteristics of the final database using summary statistics. Results: From 5256 screened records, 698 datasets were potentially eligible for inclusion; 174 datasets (5928 IPD) from 28 countries were included, 47/174 RCT datasets (1778 IPD) and 91/174 (2834 IPD) included a speech and language therapy (SLT) intervention. Participants’ median age was 63 years (interquartile range [53, 72]), 3407 (61.4%) were male and median recruitment time was 321 days (IQR 30, 1156) after stroke. IPD were available for aphasia severity or ability overall (n = 2699; 80 datasets), naming (n = 2886; 75 datasets), auditory comprehension (n = 2750; 71 datasets), functional communication (n = 1591; 29 datasets), reading (n = 770; 12 datasets) and writing (n = 724; 13 datasets). Information on SLT interventions were described by theoretical approach, therapy target, mode of delivery, setting and provider. Therapy regimen was described according to intensity (1882 IPD; 60 datasets), frequency (2057 IPD; 66 datasets), duration (1960 IPD; 64 datasets) and dosage (1978 IPD; 62 datasets). Discussion: Our international IPD archive demonstrates the application of big data principles in the context of aphasia research; our rigorous methodology for data acquisition and cleaning can serve as a template for the establishment of similar databases in other research areas.
Highlights
Collation of aphasia research data across settings, coun tries and study designs using big data principles will support analyses across different language modalities, levels of impairment, and therapy interventions in this heterogeneous population
Primary aphasia research studies in the stroke population have enrolled and analysed smaller sample sizes due to the heterogeneity of the population (Plowman et al, 2012), recruitment challenges (Wallace, 2010), the assessment of a variety of outcomes (Flowers et al, 2016), with data gathered on a range of stroke, aphasia and language measurement tools, capturing information on selected language modalities and conducting assessments in a range of different languages
We report here on the outcome of an international, multidisciplinary collaborative effort to systematically develop the REhabilitation and recovery of peopLE with Aphasia after StrokE (RELEASE) individual participant data (IPD) database, highlighting the feasibility of developing a big data post-stroke aphasia research resource and providing a template for replication by researchers in other areas of neuro-rehabilitation and communication research
Summary
Collation of aphasia research data across settings, coun tries and study designs using big data principles will support analyses across different language modalities, levels of impairment, and therapy interventions in this heterogeneous population. Big data approaches to data synthesis and analysis typically involve collation of large datasets to reveal patterns, trends, and associations which would be overlooked in single trials due to a lack of statistical power. These approaches generate data “high in volume and diversity, gathered from single participants to large groups, at one or more time points” (Auffray et al, 2016), and can be applied to a range of health care settings. The feasibility of developing a large aphasia individual participant data (IPD) database is challenging due to the differences in study designs (e.g., randomised controlled trials ‘RCTs’, case studies, cohorts), data collection time points, approaches for collecting, processing and interpreting data (Brady et al, 2016). Pooling pre-existing IPD from international studies in a large, multi-disciplinary, multi-lingual database offers a cost and time-effective (Nishimura et al, 2016) approach to addressing current aphasia research priorities (Franklin et al, 2018; Pollock et al, 2012)
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