TY - JOUR
T1 - Life course of retrospective harmonization initiatives
T2 - key elements to consider
AU - Fortier, Isabel
AU - Wey, Tina W.
AU - Bergeron, Julie
AU - Pinot de Moira, Angela
AU - Nybo-Andersen, Anne-Marie
AU - Bishop, Tom
AU - Murtagh, Madeleine J.
AU - Miocevic, Milica
AU - Swertz, Morris A.
AU - van Enckevort, Esther
AU - Marcon, Yannick
AU - Mayrhofer, Michaela. Th.
AU - Ornelas, Jos Pedro
AU - Sebert, Sylvain
AU - Santos, Ana Cristina
AU - Rocha, Artur
AU - Wilson, Rebecca C.
AU - Griffith, Lauren E.
AU - Burton, Paul
PY - 2023
Y1 - 2023
N2 - Optimizing research on the developmental origins of health and disease (DOHaD) involves implementing initiatives maximizing the use of the available cohort study data; achieving sufficient statistical power to support subgroup analysis; and using participant data presenting adequate follow-up and exposure heterogeneity. It also involves being able to undertake comparison, cross-validation, or replication across data sets. To answer these requirements, cohort study data need to be findable, accessible, interoperable, and reusable (FAIR), and more particularly, it often needs to be harmonized. Harmonization is required to achieve or improve comparability of the putatively equivalent measures collected by different studies on different individuals. Although the characteristics of the research initiatives generating and using harmonized data vary extensively, all are confronted by similar issues. Having to collate, understand, process, host, and co-analyze data from individual cohort studies is particularly challenging. The scientific success and timely management of projects can be facilitated by an ensemble of factors. The current document provides an overview of the 'life course' of research projects requiring harmonization of existing data and highlights key elements to be considered from the inception to the end of the project.
AB - Optimizing research on the developmental origins of health and disease (DOHaD) involves implementing initiatives maximizing the use of the available cohort study data; achieving sufficient statistical power to support subgroup analysis; and using participant data presenting adequate follow-up and exposure heterogeneity. It also involves being able to undertake comparison, cross-validation, or replication across data sets. To answer these requirements, cohort study data need to be findable, accessible, interoperable, and reusable (FAIR), and more particularly, it often needs to be harmonized. Harmonization is required to achieve or improve comparability of the putatively equivalent measures collected by different studies on different individuals. Although the characteristics of the research initiatives generating and using harmonized data vary extensively, all are confronted by similar issues. Having to collate, understand, process, host, and co-analyze data from individual cohort studies is particularly challenging. The scientific success and timely management of projects can be facilitated by an ensemble of factors. The current document provides an overview of the 'life course' of research projects requiring harmonization of existing data and highlights key elements to be considered from the inception to the end of the project.
KW - Data harmonization
KW - data processing
KW - longitudinal data
KW - cohort studies
KW - Developmental Origins of Health and Disease (DOHAD)
KW - DEVELOPMENTAL ORIGINS
KW - POOLED ANALYSES
KW - HEALTH
KW - PREGNANCY
U2 - 10.1017/S2040174422000460
DO - 10.1017/S2040174422000460
M3 - Journal article
C2 - 35957574
VL - 14
JO - Journal of Developmental Origins of Health and Disease
JF - Journal of Developmental Origins of Health and Disease
SN - 2040-1744
IS - 2
ER -