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。英国在长期队列研究方面也有成功的历史，这些研究旨在代表全国人口。参与者的队列数据与来自行政记录的数据之间（在征得同意的情况下）正在越来越多地建立联系，这开辟了更多的研究机会。该项目的主要目标是探索、发展和传播利用这种相互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、卫生和相关政策方面的重要问题。在此过程中，我们将解决三个具体问题：i)关联的行政数据如何帮助处理缺失的队列数据？队列数据缺失，特别是由于队列成员随着时间的推移而流失。除非这些缺失的数据在分析中得到适当处理，否则结果可能会受到偏差的影响。我们将使用来自相关管理数据的信息，这些信息可以通过适当的统计方法预测队列数据中的缺失，以帮助减少或理想地消除这种偏差。ii)关联队列数据如何提高我们对行政数据质量的理解？在队列研究数据中，可以合理地期望某些信息是正确的。这一事实可以用来评估我们在关联的管理数据中捕获相同信息的能力，在这些数据中，我们可能对准确性有更多的怀疑。我们将使用关联队列数据来评估来自行政来源的数据的质量，然后考虑任何错误分类对典型分析的潜在影响。这项工作对于这些管理数据源的许多用户非常重要，因为它将提供他们经常使用的信息的有效性。iii)关联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淆（对利益关联的替代解释）。在许多情况下，由于缺乏关于某些相关变量的信息，对行政数据的分析不能充分解释这种混淆。结果往往是由于残留的混杂而产生偏差。然而，队列数据通常包含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，从而将这些发现纳入对整个行政数据样本的分析。这项工作将使用基于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，这些研究与涉及卫生、教育和高等教育的几个行政数据源相关联。该项目的一个重要组成部分是CLS的研究人员与伦敦大学学院大奥蒙德街儿童健康研究所（ICH）的同事之间的合作。由此产生的多学科研究团队将综合各个领域的专业知识，包括社会科学、调查方法学、应用和方法统计学、数据链接方法学，以及在使用项目中使用的队列研究和管理数据集方面的丰富经验。除了通过同行评议的学术期刊和会议发言传播我们的研究结果外，我们还将为国家纵向队列研究的用户制定指导和培训，帮助他们更好地利用现有的行政数据联系。我们亦会与社会科学研究委员会紧密合作，分享研究成果，并参与更广泛的活动，以提高社会科学研究方法的价值。

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.