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Int J Epidemiol. 2017 Feb 1;46(1):103-105. doi: 10.1093/ije/dyw075.

Maelstrom Research guidelines for rigorous retrospective data harmonization.

Author information

1
Research Institute of the McGill University Health Centre, Montreal, QC, Canada.
2
McMaster University, Department of Clinical Epidemiology and Biostatistics, Hamilton, ON, Canada.
3
Eindhoven University of Technology, Department of Mathematics and Computer Science, Eindhoven, The Netherlands.
4
University Medical Center Groningen, Department of Epidemiology, Groningen, Groningen, The Netherlands.
5
McGill University, Centre of Genomics and Policy, Montreal, Montrreal, QC, Canada.
6
Ontario Institute for Cancer Research, MaRS Centre, Toronto, ON, Canada.
7
University of Michigan, Inter-university Consortium for Political and Social Research (ICPSR), Ann Arbor, MI, USA.
8
University of Bristol, D2K Research Group, School of Social and Community Medicine, Bristol, UK.

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.

KEYWORDS:

Data harmonization; data integration; data processing; individual participant data; meta-analysis; retrospective harmonization

PMID:
27272186
PMCID:
PMC5407152
DOI:
10.1093/ije/dyw075
[Indexed for MEDLINE]
Free PMC Article

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