Rigorous Training in Longitudinal Data Science (RADIANCE)

我们生活在一个我们所做的几乎每件事都被收集数据的世界。如果我们希望改善我们的健康，这些信息可能会非常有用。然而，安全地做到这一点并不容易。数据被滥用或对证据作出错误解释的例子很多。这一点在今年变得越来越明显，科学家和政府正在努力沟通他们对当前大流行理解中的不确定性。由于可用证据有限，科学家们强调，需要更多数据来比较地区、人口亚群，最重要的是，研究疫情随时间的演变。只有有了更多的数据，我们才能了解人口的变化，解释健康不平等以及时间趋势。例如，要评估地方或国家封锁是否有效，我们需要计算在特定时间段内不同社区发生了多少病例，其中有多少人住院，有多少人死亡。理想情况下，我们应该跟踪每个人从诊断到住院，再到康复或死亡，然后按地区、性别、职业和种族比较这些事件的发生率。要实现这一点，我们需要说明这些事件发生的时间。这需要随着时间的推移链接同一个人的信息。同样的原理也适用于其他疾病的研究。因此，获取相互关联的个人医疗和行政记录对于生物医学和公共卫生研究至关重要。然而，仅有数据是不够的。它们需要：(A)安全储存、清洁和准备分析；(B)适当分析；(C)与其他国家的证据和其他已发表的研究一起解释。我们将这些步骤归类为：数据管理、分析和上下文。我们的建议旨在培训健康和社会数据科学家实现这些步骤所需的核心技能。我们将使用不同的格式，这些格式都将在线，以接触到最广泛的数据科学家社区。我们将制作简短的介绍性视频(我们称之为“开胃菜”)，然后以中级和更高级的水平提供各种在线材料。其中一些将以录音讲课的形式进行，另一些将以现场辅导的形式进行，讲课所涵盖的材料将通过基于计算机的实际练习来加强。我们亦会开办专门的专题课程，包括与训练小组成员进行现场(但在网上)互动，以及让学员与我们进行一对一讨论的“数据诊所”。总括而言，我们会致力为参与管理、分析和解释复杂的纵向生物社会数据的数据科学家发展和开办一个方便和包容各方的培训计划。

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.