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The challenges in data integration - heterogeneity and complexity in clinical trials and patient registries of Systemic Lupus Erythematosus.
Le Sueur, Helen; Bruce, Ian N; Geifman, Nophar.
Afiliación
  • Le Sueur H; Centre for Health Informatics, Vaughan Housue, Portsmouth St., The University of Manchester, Manchester, M13 9GB, UK.
  • Bruce IN; Arthritis Research UK Centre for Epidemiology, The University of Manchester, Manchester, UK.
  • Geifman N; NIHR Manchester Biomedical Research Centre, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
BMC Med Res Methodol ; 20(1): 164, 2020 06 24.
Article en En | MEDLINE | ID: mdl-32580708
ABSTRACT

BACKGROUND:

Individual clinical trials and cohort studies are a useful source of data, often under-utilised once a study has ended. Pooling data from multiple sources could increase sample sizes and allow for further investigation of treatment effects; even if the original trial did not meet its primary goals. Through the MASTERPLANS (MAximizing Sle ThERapeutic PotentiaL by Application of Novel and Stratified approaches) national consortium, focused on Systemic Lupus Erythematosus (SLE), we have gained valuable real-world experiences in aligning, harmonising and combining data from multiple studies and trials, specifically where standards for data capture, representation and documentation, were not used or were unavailable. This was not without challenges arising both from the inherent complexity of the disease and from differences in the way data were captured and represented across different studies. MAIN BODY Data were, unavoidably, aligned by hand, matching up equivalent or similar patient variables across the different studies. Heterogeneity-related issues were tackled and data were cleaned, organised and combined, resulting in a single large dataset ready for analysis. Overcoming these hurdles, often seen in large-scale data harmonization and integration endeavours of legacy datasets, was made possible within a realistic timescale and limited resource by focusing on specific research questions driven by the aims of MASTERPLANS. Here we describe our experiences tackling the complexities in the integration of large, diverse datasets, and the lessons learned.

CONCLUSIONS:

Harmonising data across studies can be complex, and time and resource consuming. The work carried out here highlights the importance of using standards for data capture, recording, and representation, to facilitate both the integration of large datasets and comparison between studies. Where standards are not implemented at the source harmonisation is still possible by taking a flexible approach, with systematic preparation, and a focus on specific research questions.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Lupus Eritematoso Sistémico Tipo de estudio: Guideline / Observational_studies Límite: Humans Idioma: En Revista: BMC Med Res Methodol Asunto de la revista: MEDICINA Año: 2020 Tipo del documento: Article País de afiliación: Reino Unido

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Lupus Eritematoso Sistémico Tipo de estudio: Guideline / Observational_studies Límite: Humans Idioma: En Revista: BMC Med Res Methodol Asunto de la revista: MEDICINA Año: 2020 Tipo del documento: Article País de afiliación: Reino Unido