Quality, quantity and harmony: the DataSHaPER approach to integrating data across bioclinical studies

Background Vast sample sizes are often essential in the quest to disentangle the complex interplay of the genetic, lifestyle, environmental and social factors that determine the aetiology and progression of chronic diseases. The pooling of information between studies is therefore of central importan...

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Bibliographic Details
Published in:	International journal of epidemiology Vol. 39; no. 5; pp. 1383 - 1393
Main Authors:	Fortier, Isabel, Burton, Paul R, Robson, Paula J, Ferretti, Vincent, Little, Julian, L’Heureux, Francois, Deschênes, Mylène, Knoppers, Bartha M, Doiron, Dany, Keers, Joost C, Linksted, Pamela, Harris, Jennifer R, Lachance, Geneviève, Boileau, Catherine, Pedersen, Nancy L, Hamilton, Carol M, Hveem, Kristian, Borugian, Marilyn J, Gallagher, Richard P, McLaughlin, John, Parker, Louise, Potter, John D, Gallacher, John, Kaaks, Rudolf, Liu, Bette, Sprosen, Tim, Vilain, Anne, Atkinson, Susan A, Rengifo, Andrea, Morton, Robin, Metspalu, Andres, Wichmann, H Erich, Tremblay, Mark, Chisholm, Rex L, Garcia-Montero, Andrés, Hillege, Hans, Litton, Jan-Eric, Palmer, Lyle J, Perola, Markus, Wolffenbuttel, Bruce HR, Peltonen, Leena, Hudson, Thomas J
Format:	Journal Article
Language:	English
Published:	Oxford Oxford University Press 01-10-2010
Subjects:	Analysis. Health state Biological and medical sciences Clinical Trials as Topic - statistics & numerical data Data Collection - methods Data Interpretation, Statistical data pooling data quality Data synthesis DataSHaPER Epidemiologic Methods Epidemiology General aspects harmonization Health Behavior Humans Information Storage and Retrieval - methods Medical sciences Medicin och hälsovetenskap meta-analysis Meta-Analysis as Topic Phoebe prospective harmonization Public health. Hygiene Public health. Hygiene-occupational medicine Residence Characteristics retrospective harmonization Socioeconomic Factors Theory and Methods Europe Human DataSHaPER prospective harmonization Chronic disease Data synthesis Evidence-based practice meta-analysis data pooling Epidemiology Evidence-based medicine Retrospective Harmonization Metaanalysis Prospective data quality Synthesis Quality Observation data retrospective harmonization Public health
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Summary:	Background Vast sample sizes are often essential in the quest to disentangle the complex interplay of the genetic, lifestyle, environmental and social factors that determine the aetiology and progression of chronic diseases. The pooling of information between studies is therefore of central importance to contemporary bioscience. However, there are many technical, ethico-legal and scientific challenges to be overcome if an effective, valid, pooled analysis is to be achieved. Perhaps most critically, any data that are to be analysed in this way must be adequately ‘harmonized’. This implies that the collection and recording of information and data must be done in a manner that is sufficiently similar in the different studies to allow valid synthesis to take place. Methods This conceptual article describes the origins, purpose and scientific foundations of the DataSHaPER (DataSchema and Harmonization Platform for Epidemiological Research; http://www.datashaper.org), which has been created by a multidisciplinary consortium of experts that was pulled together and coordinated by three international organizations: P3G (Public Population Project in Genomics), PHOEBE (Promoting Harmonization of Epidemiological Biobanks in Europe) and CPT (Canadian Partnership for Tomorrow Project). Results The DataSHaPER provides a flexible, structured approach to the harmonization and pooling of information between studies. Its two primary components, the ‘DataSchema’ and ‘Harmonization Platforms’, together support the preparation of effective data-collection protocols and provide a central reference to facilitate harmonization. The DataSHaPER supports both ‘prospective’ and ‘retrospective’ harmonization. Conclusion It is hoped that this article will encourage readers to investigate the project further: the more the research groups and studies are actively involved, the more effective the DataSHaPER programme will ultimately be.
Bibliography:	istex:3A9862D456000A9B0D3CEFD30D0A3A80B7C5749F ArticleID:dyq139 ark:/67375/HXZ-3Z12PK5R-J ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0300-5771 1464-3685 1464-3685
DOI:	10.1093/ije/dyq139