Linkage of national longitudinal cohort studies and administrative data: A mutually beneficial arrangement (Link-AMBA)

近几十年来，政府和公共机构（例如学校和NHS）为研究目的创建的行政数据越来越多。英国也有长期队列研究的成功历史，这些研究旨在代表全国人口。越来越多的参与者的队列数据和他们的行政记录数据之间的联系（经同意），这开辟了更多的研究机会，该项目的主要目标是探索，开发和传播利用这种联系的国家纵向队列和行政数据的方法，以帮助更好地解决社会，健康和相关政策的重要问题。在此过程中，我们将解决三个具体问题：i）关联的管理数据如何帮助处理缺失的队列数据？队列遭受数据缺失，特别是由于队列成员随时间推移而流失。除非在分析中适当处理这些缺失数据，否则结果可能会受到偏倚的影响。我们将使用来自关联管理数据的信息，这些信息可以预测队列数据中的缺失，以适当的统计方法来帮助减少或理想地消除这种偏倚。可以合理预期队列研究数据中的某些信息是正确的。这一事实可以用来评估我们如何在关联的行政数据中捕获相同的信息，我们可能对准确性有更多的怀疑。我们将使用链接的队列数据来评估来自管理来源的数据质量，然后考虑任何错误分类对典型分析的潜在影响。这项工作对于这些行政数据源的许多用户来说非常重要，因为它将提供他们经常使用的信息的有效性。iii）链接的队列数据如何帮助解决行政数据分析中的残留混淆？统计分析通常试图解释混杂因素（对相关性的替代解释）。在许多情况下，由于缺乏关于某些相关变量的信息，对行政数据的分析无法充分说明这种混淆。结果往往是由于残留混杂的偏倚。然而，队列数据通常包含关于潜在混杂因素的丰富信息。我们将使用关联的队列和管理数据，并将仅使用管理数据中的变量的分析结果与额外使用队列数据中的变量的分析结果进行比较。然后，我们将探索替代方法，将这些发现纳入整个行政数据样本的分析中。这项工作将使用来自伦敦大学学院纵向研究中心（CLS）的国家纵向队列研究的数据进行，该研究与多个行政数据源相关，涵盖健康、教育、该项目的一个重要组成部分是CLS的研究人员和UCL大奥蒙德街儿童健康研究所（ICH）的同事之间的合作。研究团队将结合多个领域的专业知识，包括社会科学、调查方法、应用和方法统计、数据连接方法，以及使用项目中使用的队列研究和行政数据集的丰富经验。除了通过同行评审的学术期刊和会议报告传播我们的研究结果外，我们将为国家纵向队列研究的用户提供指导和培训，帮助他们更好地利用现有的行政数据链接。我们还将与ESRC密切合作，分享从项目中吸取的经验教训，并参与更广泛的活动，以提高人们对社会科学研究方法价值的认识。

Over recent decades, there has been increasing availability of administrative data created by government and public bodies (for example schools and the NHS) for research purposes. The UK also has a successful history of long-running cohort studies which are designed to be representative of the national population. Increasingly, linkages are being made (with consent) between participants' cohort data and their data from administrative records, which open up additional research opportunities.The main objective of this project is to explore, develop and disseminate methods which utilise such linked national longitudinal cohort and administrative data to help better address important questions for society, health, and related policies. In doing this, we will address three specific questions:i) How can linked administrative data aid the handling of missing cohort data? Cohorts suffer from missing data, particularly due to attrition of cohort members over time. Unless these missing data are appropriately handled in analyses, results may be affected by bias. We will use information from linked administrative data which is predictive of missingness in the cohort data in appropriate statistical methods to help reduce, or ideally eliminate, this bias.ii) How can linked cohort data improve our understanding of the quality of administrative data? Some information can be reasonably expected to be correct in cohort study data. This fact can be utilised to assess how well we capture this same information in linked administrative data, where we may have more doubts over accuracy. We will use linked cohort data to assess the quality of data from administrative sources, then consider the potential impact of any misclassification on typical analyses. This work is important for the many users of these administrative data sources as it will provide validity of the information they frequently use.iii) How can linked cohort data help address residual confounding in analyses of administrative data? Statistical analyses generally try to account for confounding (alternative explanations for an association of interest). There are many instances where analyses of administrative data cannot sufficiently account for such confounding due to lack of information about certain relevant variables. The consequence is often bias due to residual confounding. Cohort data, however, will often contain rich information on potential confounders. We will use linked cohort and administrative data and compare results from analyses that use only variables in the administrative data with those additionally using variables in the cohort data. We will then explore alternative approaches whereby these findings can be incorporated into analyses of the whole administrative data sample.This work will be undertaken using data from national longitudinal cohort studies based at the UCL Centre for Longitudinal Studies (CLS) linked to several administrative data sources covering health, education, and higher education.An important part of the project is the initiation of a collaboration between researchers at CLS and colleagues from UCL Great Ormond Street Institute of Child Health (ICH). The resultant multidisciplinary research team will have combined expertise across a variety of areas including social science, survey methodology, applied and methodological statistics, and data linkage methodology, as well as extensive experience of using the cohort studies and administrative datasets being utilised in the project.As well as disseminating our findings via peer-reviewed academic journals and conference presentations, we will develop guidance and training for users of the national longitudinal cohort studies to help them better utilise the available administrative data linkages.We will also work closely with the ESRC to share learning from the project and participate in wider activities to raise awareness of the value of social science research methods.