Data Harmonization and Data Pooling from Cohort Studies: A Practical Approach for Data Management

Data pooling from pre-existing multiple datasets can be useful to increase study sample size and statistical power to answer a research question. However, individual datasets may contain variables that measure the same construct differently, posing challenges for data pooling. Variable harmonization...

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Vydáno v:International journal of population data science Ročník 6; číslo 1; s. 1680
Hlavní autoři: Adhikari, Kamala, Patten, Scott B, Patel, Alka B, Premji, Shahirose, Tough, Suzanne, Letourneau, Nicole, Giesbrecht, Gerald, Metcalfe, Amy
Médium: Journal Article
Jazyk:angličtina
Vydáno: Wales Swansea University 01.01.2021
Témata:
Alberta
Cohort Studies
Data Collection
Data Management
Female
Humans
Population Data Science
Pregnancy
Sample Size
Alberta
cohort studies
data pooling or combination
comparable dataset
data harmonization
harmonization strategies
ISSN:2399-4908, 2399-4908
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Shrnutí:Data pooling from pre-existing multiple datasets can be useful to increase study sample size and statistical power 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– the 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 synchronized across the datasets considering the frequency of measurement, the timing of measurement, 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. Variable harmonization and pooling provide an opportunity to increase study power and the utility of existing data, permitting 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.
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Competing interests: The authors declare that they have no competing interests.
ISSN:2399-4908
2399-4908
DOI:10.23889/ijpds.v6i1.1680