Optimising cohort data in Europe

The reported benefits of integrating various types, levels and sources of data in such a way that they can be made compatible and comparable, and thus increasing their value and usefulness for specific research purposes, include: y y Increased sample size and, consequently, improved statistical power. y y Improved generalisability of results. y y Increased ability to investigate effect heterogeneity due to more diversity among participants. y y Answering novel research questions that cannot be answered by individual cohorts. y y Helping to ensure the validity of comparative research and reproducibility. y y Encouraging more efficient use of existing data, time and resources. Because of all these benefits, more and more international research initiatives have incorporated data harmonisation into their design to investigate and/or identify risk or protector factors for health outcomes (Harris et al., 2012; Magnin et al., 2020). Some of these initiatives aim to highlight the value of health data through large-scale analysis and to ensure the development of harmonised measures and standardised information infrastructures. The ability to effectively harmonise data from different cohort studies facilitates the rapid extraction of new scientific knowledge on the emergence and progression of diseases and conditions and the classification of their respective phenotypes. This approach has the ambition to address the unmet needs raised by many pathological conditions, including the identification and the following validation of new biomarkers, phenotypic and genotypic stratification of patients to different treatments, as well as screening and selection of novel therapy targets (Zhang et al., 2016; Kouroru et al., 2018). 2.1. Strategies Before focusing on particular and case-specific harmonisation methods, one has to consider the different available harmonisation strategies, which can be derived from the literature. The use of one strategy or another will depend on the stage and condition of the available cohort data rather than on an arbitrary researchers' choice. Three types of harmonisation strategies can be differentiated (Granda et al., 2016): prospective harmonisation, ex ante retrospective harmonisation and ex post retrospective harmonisation. 2.1.1. Prospective harmonisation In this strategy, studies share the same study design, questionnaires and instruments for collecting measures (e.g. biological, psychological and/or social). Some adaptations may occur for individual data collection sites, but the goal is to maintain comparability y y Providing opportunities for collaborative and multicentre research. y y Bringing together expert knowledge across disciplinary boundaries.