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 harmonizatio...

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Autores principales:	Kamala Adhikari, Scott B Patten, Alka B Patel, Shahirose Premji, Suzanne Tough, Nicole Letourneau, Gerald Giesbrecht, Amy Metcalfe
Formato:	article
Lenguaje:	EN
Publicado:	Swansea University 2021
Materias:	Demography. Population. Vital events HB848-3697
Acceso en línea:	https://doaj.org/article/f0b734d3454b4ffc9f9840f3a51493db
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spelling	oai:doaj.org-article:f0b734d3454b4ffc9f9840f3a51493db2021-12-03T15:47:28ZData Harmonization and Data Pooling from Cohort Studies: A Practical Approach for Data Management10.23889/ijpds.v6i1.16802399-4908https://doaj.org/article/f0b734d3454b4ffc9f9840f3a51493db2021-11-01T00:00:00Zhttps://ijpds.org/article/view/1680https://doaj.org/toc/2399-4908 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. Kamala AdhikariScott B Patten Alka B Patel Shahirose Premji Suzanne Tough Nicole Letourneau Gerald Giesbrecht Amy Metcalfe Swansea UniversityarticleDemography. Population. Vital eventsHB848-3697ENInternational Journal of Population Data Science, Vol 6, Iss 1 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Demography. Population. Vital events HB848-3697
spellingShingle	Demography. Population. Vital events HB848-3697 Kamala Adhikari Scott B Patten Alka B Patel Shahirose Premji Suzanne Tough Nicole Letourneau Gerald Giesbrecht Amy Metcalfe Data Harmonization and Data Pooling from Cohort Studies: A Practical Approach for Data Management
description	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.
format	article
author	Kamala Adhikari Scott B Patten Alka B Patel Shahirose Premji Suzanne Tough Nicole Letourneau Gerald Giesbrecht Amy Metcalfe
author_facet	Kamala Adhikari Scott B Patten Alka B Patel Shahirose Premji Suzanne Tough Nicole Letourneau Gerald Giesbrecht Amy Metcalfe
author_sort	Kamala Adhikari
title	Data Harmonization and Data Pooling from Cohort Studies: A Practical Approach for Data Management
title_short	Data Harmonization and Data Pooling from Cohort Studies: A Practical Approach for Data Management
title_full	Data Harmonization and Data Pooling from Cohort Studies: A Practical Approach for Data Management
title_fullStr	Data Harmonization and Data Pooling from Cohort Studies: A Practical Approach for Data Management
title_full_unstemmed	Data Harmonization and Data Pooling from Cohort Studies: A Practical Approach for Data Management
title_sort	data harmonization and data pooling from cohort studies: a practical approach for data management
publisher	Swansea University
publishDate	2021
url	https://doaj.org/article/f0b734d3454b4ffc9f9840f3a51493db
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Data Harmonization and Data Pooling from Cohort Studies: A Practical Approach for Data Management

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