Evidence map›Paper›PMID 34888420›Full record

ArticleInternational journal of population data science2021

Data harmonization and data pooling from cohort studies: a practical approach for data management.

Kamala Adhikari, Scott B Patten, Alka B Patel, Shahirose Premji, Suzanne Tough, Nicole Letourneau, Gerald Giesbrecht, Amy Metcalfe

Abstract read
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In one paragraph

Article in International journal of population data science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 1 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

42 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

8 authors.

Kamala AdhikariDepartment of Community Health Sciences, University of Calgary, Calgary, Canada.
Scott B PattenDepartment of Community Health Sciences, University of Calgary, Calgary, Canada.
Alka B PatelDepartment of Community Health Sciences, University of Calgary, Calgary, Canada.
Shahirose PremjiSchool of Nursing, Faculty of Health, York University, Calgary, Canada.
Suzanne ToughDepartment of Community Health Sciences, University of Calgary, Calgary, Canada.
Nicole LetourneauDepartment of Community Health Sciences, University of Calgary, Calgary, Canada.
Gerald GiesbrechtDepartment of Community Health Sciences, University of Calgary, Calgary, Canada.
Amy MetcalfeDepartment of Community Health Sciences, University of Calgary, Calgary, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Data pooling from pre-existing datasets can be useful to increase study sample size and statistical power in order to answer a research question. However, individual datasets may contain variables that measure the same construct differently, posing challenges for data pooling. Variable harmonization, an approach that can generate comparable datasets from heterogeneous sources, can address this issue in some circumstances. As an illustrative example, this paper describes the data harmonization strategies that helped generate comparable datasets across two Canadian pregnancy cohort studies: All Our Families; and the Alberta Pregnancy Outcomes and Nutrition. Variables were harmonized considering multiple features across the datasets: the construct measured; question asked/response options; the measurement scale used; the frequency of measurement; timing of measurement, and the data structure. Completely matching, partially matching, and completely un-matching variables across the datasets were determined based on these features. Variables that were an exact match were pooled as is. Partially matching variables were harmonized or processed under a common format across the datasets considering the frequency of measurement, the timing of measurement, the measurement scale used, and response options. Variables that were completely unmatching could not be harmonized into a single variable. The variable harmonization strategies that were used to generate comparable cohort datasets for data pooling are applicable to other data sources. Future studies may employ or evaluate these strategies, which permit researchers to answer novel research questions in a statistically efficient, timely, and cost-efficient manner that could not be achieved using a single data source.

Indexed as

Data ManagementAlbertaCohort StudiesData CollectionFemaleHumansPregnancySample Sizecohort studiescomparable datasetdata harmonizationdata pooling or combinationharmonization strategies

Identifiers

PMID34888420
PMCPMC8631396

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.