Is rigorous retrospective harmonization possible? Application of the DataSHaPER approach across 53 large studies | Zendy

Isabel Fortier | Zendy; Dany Doiron | Zendy; Julian Little | Zendy; Vincent Ferretti | Zendy; François L’Heureux | Zendy; Ronald P. Stolk | Zendy; Bartha Maria Knoppers | Zendy; Thomas J. Hudson | Zendy; Paul R. Burton | Zendy

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Is rigorous retrospective harmonization possible? Application of the DataSHaPER approach across 53 large studies

Author(s) -

Isabel Fortier,

Dany Doiron,

Julian Little,

Vincent Ferretti,

François L’Heureux,

Ronald P. Stolk,

Bartha Maria Knoppers,

Thomas J. Hudson,

Paul R. Burton

Publication year - 2011

Publication title -

international journal of epidemiology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.406

H-Index - 208

eISSN - 1464-3685

pISSN - 0300-5771

DOI - 10.1093/ije/dyr106

Subject(s) - harmonization , observational study , construct (python library) , standardization , equivalence (formal languages) , population , data collection , computer science , clinical study design , variable (mathematics) , medicine , environmental health , management science , statistics , mathematics , pathology , engineering , clinical trial , mathematical analysis , physics , discrete mathematics , acoustics , programming language , operating system

Proper understanding of the roles of, and interactions between genetic, lifestyle, environmental and psycho-social factors in determining the risk of development and/or progression of chronic diseases requires access to very large high-quality databases. Because of the financial, technical and time burdens related to developing and maintaining very large studies, the scientific community is increasingly synthesizing data from multiple studies to construct large databases. However, the data items collected by individual studies must be inferentially equivalent to be meaningfully synthesized. The DataSchema and Harmonization Platform for Epidemiological Research (DataSHaPER; http://www.datashaper.org) was developed to enable the rigorous assessment of the inferential equivalence, i.e. the potential for harmonization, of selected information from individual studies.

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