Cited by 101Research Data Management PracticesStatistical Methods in EpidemiologyHealth, Environment, Cognitive Aging

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

Open access
Fortier, Isabel, Doiron, Dany, Little, Julian, Ferretti, Vincent, L’Heureux, François, Stolk, Ronald P, Knoppers, Bartha M, Hudson, Thomas J +1 more·2011·International Journal of Epidemiology

Abstract

BACKGROUND: 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. METHODS: This article examines the value of using the DataSHaPER for retrospective harmonization of established studies. Using the DataSHaPER approach, the potential to generate 148 harmonized variables from the questionnaires and physical measures collected in 53 large population-based studies (6.9 million participants) was assessed. Variable and study characteristics that might influence the potential for data synthesis were also explored. RESULTS: Out of all assessment items evaluated (148 variables for each of the 53 studies), 38% could be harmonized. Certain characteristics of variables (i.e. relative importance, individual targeted, reference period) and of studies (i.e. observational units, data collection start date and mode of questionnaire administration) were associated with the potential for harmonization. For example, for variables deemed to be essential, 62% of assessment items paired could be harmonized. CONCLUSION: The current article shows that the DataSHaPER provides an effective and flexible approach for the retrospective harmonization of information across studies. To implement data synthesis, some additional scientific, ethico-legal and technical considerations must be addressed. The success of the DataSHaPER as a harmonization approach will depend on its continuing development and on the rigour and extent of its use. The DataSHaPER has the potential to take us closer to a truly collaborative epidemiology and offers the promise of enhanced research potential generated through synthesized databases.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/ije/dyr106

Cites

21 of 21 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Fortier, I., Doiron, D., Little, J., Ferretti, V., L’Heureux, F., Stolk, R. P., Knoppers, B. M., Hudson, T. J., & Burton, P. R.
Affiliations
  • McGill University Health Centre (CA)
  • Ontario Institute for Cancer Research (CA)
Corresponding author
Fortier, Isabel
Venue
International Journal of Epidemiology
Published
2011-07-30
Volume
40
Issue
5
Pages
1314-1328
ISSN
  • 0300-5771 (ISSN-L)
  • 1464-3685
OpenAlex
W2117662116
Cited by
101
References
21
Citations by year
2026: 3 · 2025: 2 · 2024: 10 · 2023: 6 · 2022: 13 · 2021: 7 · 2020: 4 · 2019: 4 · 2018: 7 · 2017: 5 · 2016: 12 · 2015: 7 · 2014: 6 · 2013: 7 · 2012: 7
FWCI
19.28
Type
journal article
Language
en
Open access
Licence
other-oa
Locations
  • International Journal of Epidemiology (publishedVersion, other-oa)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Leicester Research Archive (University of Leicester) (submittedVersion)
  • HighWire Press Open Archive (submittedVersion)
  • ANU Open Research (Australian National University) (submittedVersion)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
  • Bristol Research (University of Bristol) (submittedVersion)
Funders
  • Wellcome Trust (086160/Z/08/A)
  • Agence Nationale de la Recherche (ANR-11-INBS-0002)
  • University of Western Sydney
  • National Institutes of Health
Topics
  • Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
Keywords
data synthesis, questionnaire administration mode, chronic disease risk factors
MeSH terms
Chronic Disease, Data Collection, Data Interpretation, Statistical, Epidemiologic Methods, Humans, Surveys and Questionnaires, Retrospective Studies, Risk Factors, Cohort Studies, Databases, Factual

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review