Evidence map›Paper›PMID 27272186›Full record

ArticleInternational journal of epidemiology2017

Maelstrom Research guidelines for rigorous retrospective data harmonization.

Isabel Fortier, Parminder Raina, Edwin R Van den Heuvel, Lauren E Griffith, Camille Craig, Matilda Saliba, Dany Doiron, Ronald P Stolk, Bartha M Knoppers, Vincent Ferretti and 2 more

Abstract read
In one paragraph

Article in International journal of epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 181 papers, 12 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
181citing papers in PubMed, 12 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

181 citing papers in PubMed, 12 syntheses or guidelines pooled it.

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  13. Quantitative amyloid PET in Alzheimer's disease: the AMYPAD prognostic and natural history study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2020
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121 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Isabel FortierResearch Institute of the McGill University Health Centre, Montreal, QC, Canada.
Parminder RainaMcMaster University, Department of Clinical Epidemiology and Biostatistics, Hamilton, ON, Canada.
Edwin R Van den HeuvelEindhoven University of Technology, Department of Mathematics and Computer Science, Eindhoven, The Netherlands.
Lauren E GriffithMcMaster University, Department of Clinical Epidemiology and Biostatistics, Hamilton, ON, Canada.
Camille CraigResearch Institute of the McGill University Health Centre, Montreal, QC, Canada.
Matilda SalibaResearch Institute of the McGill University Health Centre, Montreal, QC, Canada.
Dany DoironResearch Institute of the McGill University Health Centre, Montreal, QC, Canada.
Ronald P StolkUniversity Medical Center Groningen, Department of Epidemiology, Groningen, Groningen, The Netherlands.
Bartha M KnoppersMcGill University, Centre of Genomics and Policy, Montreal, Montrreal, QC, Canada.
Vincent FerrettiOntario Institute for Cancer Research, MaRS Centre, Toronto, ON, Canada.
Peter GrandaUniversity of Michigan, Inter-university Consortium for Political and Social Research (ICPSR), Ann Arbor, MI, USA.
Paul BurtonUniversity of Bristol, D2K Research Group, School of Social and Community Medicine, Bristol, UK.

Funding

Medical Research CouncilWellcome Trust
6 · The paper itself

Abstract

Background: It is widely accepted and acknowledged that data harmonization is crucial: in its absence, the co-analysis of major tranches of high quality extant data is liable to inefficiency or error. However, despite its widespread practice, no formalized/systematic guidelines exist to ensure high quality retrospective data harmonization. Methods: To better understand real-world harmonization practices and facilitate development of formal guidelines, three interrelated initiatives were undertaken between 2006 and 2015. They included a phone survey with 34 major international research initiatives, a series of workshops with experts, and case studies applying the proposed guidelines. Results: A wide range of projects use retrospective harmonization to support their research activities but even when appropriate approaches are used, the terminologies, procedures, technologies and methods adopted vary markedly. The generic guidelines outlined in this article delineate the essentials required and describe an interdependent step-by-step approach to harmonization: 0) define the research question, objectives and protocol; 1) assemble pre-existing knowledge and select studies; 2) define targeted variables and evaluate harmonization potential; 3) process data; 4) estimate quality of the harmonized dataset(s) generated; and 5) disseminate and preserve final harmonization products. Conclusions: This manuscript provides guidelines aiming to encourage rigorous and effective approaches to harmonization which are comprehensively and transparently documented and straightforward to interpret and implement. This can be seen as a key step towards implementing guiding principles analogous to those that are well recognised as being essential in securing the foundational underpinning of systematic reviews and the meta-analysis of clinical trials.

Indexed as

Retrospective StudiesBiomedical ResearchData CollectionData Interpretation, StatisticalGuidelines as TopicHumansResearch DesignData harmonizationdata integrationdata processingindividual participant datameta-analysisretrospective harmonization

Identifiers

PMID27272186
PMCPMC5407152

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.