Rigorous Training in Longitudinal Data Science (RADIANCE)

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We live in a world where data are collected on nearly everything we do. Such information has the potential to be extremely useful if we wish to improve our health. However, doing this safely is not easy. There are many examples where data have been misused or erroneous interpretation of the evidence has been drawn. This is increasingly apparent this year, where scientists and governments are struggling to communicate the uncertainties in their understanding of the current pandemic. Because the available evidence is limited, scientists stress that more data are needed to compare regions, subgroups of people and, crucially, study the evolution of the epidemic over time. Only with more data we will be able to understand variations in the population and explain health inequalities as well as time trends. For example, to assess if a local or national lock-down is working, we need to count how many cases arise in different communities over certain periods of time, how many of them are hospitalised, and how many die. Ideally, we should follow each individual from diagnosis, to hospitalization, to recovery or death, and then compare the incidence of each of these events, by region, sex, occupation, and ethnicity, for example. To achieve this, we need to account for when each of these events occurs. This requires linking information on the same individual over time. The same principle applies to the study of other diseases. For this reason, access to linked individual medical and administrative records is crucial for biomedical and public health research. Having the data is not sufficient, however. They need to be: (a) safely stored, cleaned, and prepared for analysis; (b) properly analysed; and (c) interpreted together with evidence from other countries and other published research. We label these steps: data stewardship, analysis, and context. Our proposal aims to train health and social data scientists in the core skills needed to achieve these steps. We will use different formats which will all be on-line to reach the broadest community of data scientists. We will produce short introductory videos (which we call "Appetisers"), and then various on-line material delivered at an intermediate and more advanced levels. Some of this will be in the form of recorded lectures, some as live tutorials where the material covered by the lectures is reinforced with practical computer-based exercises. We will also run specific courses on specialised topics which will include live (but on-line) interactions with members of the training team, and "data clinics" where participants can have one-to-one discussions with us.In summary, we will endeavour to develop and run an accessible and inclusive training programme for data scientists involved in the management, analysis and interpretation of complex longitudinal biosocial data.