Optimising cohort data in Europe

from the beginning of the process. This is typically used in multicentre studies where researchers from different centres come together to decide upon the same study design, questionnaires and/or instruments for collecting measures. The advantages are clear to see, as the process of harmonisation is established in the beginning and the integration of the data into one combined dataset can be easily achieved. Nevertheless, in prospective harmonisation difficulties may be encountered in the management of the different participating centres, and there may still be differences between study designs and data collection for cultural reasons, or unequal diligence on the part of the research teams. Involvement and support by funding agencies is likely to be essential to the success of the prospective harmonisation approach (Chandler et al., 2015). Funding agencies may be best suited to initiate and coordinate data harmonisation initiatives because of their comprehensive knowledge of research in particular areas, potential to leverage additional resources, ability to encourage collaboration among researchers, and unique perspective on goals that extend beyond individual research projects. 2.1.2. Ex ante retrospective harmonisation This strategy combines data from cohort studies that were not specifically designed to be comparable, but through the use of standard collection tools and operating procedures, their data can be integrated. Different standards are available for collecting and storing data, such as the Clinical Data Interchange Standard Consortium (CDISC) (https://www.cdisc.org) standards for clinical trials, the Global Alliance for Genomics and Health (GA4GH) (https://www.ga4gh.org) standards for genomic data, the Observational Medical Outcomes Partnership (OMOP) standards established within the Observational Health Data Sciences and Informatics (OHDSI) (https://www.ohdsi.org) initiative for observational studies etc. Nevertheless, in practice, the standards in cohort research are rather set by specific cohort studies that are recognised for their innovation and impact in a certain research context. Such studies provide an example for relating future studies in terms of the surveys and measuring instruments to be used (e.g. the Health and Retirement Survey, https://g2aging.org/). It should be noted that the rapid development of new technologies (e.g. array-based gene expression surpassed by RNA seq) (Wijmenga and Zhernakova, 2018) can make standardisation difficult even within cohorts, and under such circumstances, tailor-made methods should be developed to harmonise data produced by different technologies (Rudy and Valafar, 2011; Borisov et al., 2017). As far as possible, it may be useful to create transformation standards or links between different standards on the same measured variables in order to have available equivalent scores for various scales that measure the same health construct. 2.1.3. Ex post retrospective harmonisation Ex post retrospective harmonisation also combines data from cohort studies that were not specifically designed to be comparable but, even though no standard formats or protocols have been used, variables can be assessed and edited to achieve commonality