Optimising cohort data in Europe

1. Introduction SYNCHROS (SYNergies for Cohorts in Health: integrating the Role of all Stakeholders) is an EU Horizon 2020 project that aims to identify the methodological, practical, legal and ethical barriers and opportunities in cohort research. The main aim of SYNCHROS is to formulate a sustainable European strategy for the next generation of integrated cohorts. In so doing, SYNCHROS supports developments for a stratified and personalised medicine approach and facilitates health policy. In relation to the methodological domain of the SYNCHROS project, it is essential to identify the methodological problems faced by cohort researchers as well as provide solutions and recommendations for research practice. A definition of cohort study has to be agreed upon prior to describing and contextualising the key issues of data harmonisation, integration and optimisation, including the main methodological challenges, best practices, and implications for implementation. A cohort study refers to a specific longitudinal type of study design. It usually involves a group of people who share a common characteristic, such as living in a specific location or experiencing a defining event in a selected period (such as birth), and performs cross sectional repeated assessments of the studied outcomes at pre-defined intervals over time. A cohort study can be either retrospective or prospective. In the retrospective case, the study relates to data collected in the past (e.g. medical records). In the prospective case, the cohort study relies on the collection of new data. SYNCHROS created a repository (https://repository.synchros.eu) to share key information on initiatives that harmonise and/or integrate cohort data. The SYNCHROS repository initiatives include information about harmonisation strategies and the different types of analytical approaches to produce integrated results between different cohorts; details about these strategies and approaches are presented in this chapter. 2. Cohort data harmonisation and integration First of all, it is necessary to clarify the differences between the concepts of harmonising and integrating data from different cohort studies. Harmonisation can be understood as practices that improve the comparability of variables from different studies and thus reduce the heterogeneity across studies (Maelstrom Research). Integration, on the other hand, is understood as the act or process of combining or pooling similar data from different studies into a unified whole. Therefore, harmonisation of data from different cohorts is a first necessary step, which allows these data to be integrated into the same dataset, and thus increase their value and usefulness for specific research purposes (Lesko et al., 2018).