Using Matched Cohort Methods to Investigate the Risk of Respiratory Diseases in Survivors of Adult Cancer

越来越多的研究正在使用患者与任何医疗服务（例如GP或医院访问）交互时生成的健康数据进行。这些数据可用于进行比较流行病学研究，以调查患病个体（暴露组）与无病史者（未暴露组）相比的不良健康后果风险。在这种情况下，通常使用配对队列方法来选择未暴露组。在该方法中，暴露个体与一个或多个未暴露个体在一个或多个患者特征（例如年龄和性别）上匹配。从理论上讲，这将允许暴露和未暴露队列之间的偏差较小的比较，因为匹配将使各组彼此更加相似。然而，这一假设尚未得到验证;很少有人知道什么时候应该使用匹配的队列设计，匹配应该如何进行，应该使用什么样的特征来匹配individual.The主要目的是调查一个现实世界的流行病学研究问题。特别是，我们的目标是量化有癌症史的个体与没有癌症史的个体相比患呼吸系统疾病的风险，作为使用大数据确定癌症幸存者疾病预防领域的更大雄心的一部分。次要目标是评估进行和分析匹配队列研究的最佳方法，以进行长期结局的临床研究。第一步：进行描述性系统性文献综述，系统性地综述癌症与后续呼吸系统疾病之间相关性的证据。这一步将用于定义感兴趣的特定主要研究呼吸结局。第2步：进行叙述性综述，了解匹配队列方法在流行病学研究中的使用情况，以及选择该研究设计的当前原因，并确定当前研究差距。第3步：进行模拟研究。我们将生成一个实验模拟数据集，它将作为一个“受控环境”，我们将能够在其中进行实验，以测试支持匹配队列设计的假设。将从叙述性审查中确定将要解决的具体研究问题（步骤2）。该阶段的目的是为匹配队列研究创建最佳匹配策略。这一阶段的结果将用于开发开放获取的编码宏，可供其他研究人员用于进行匹配的队列研究。这一步将通过开发可应用于许多不同研究领域的数据分析技术来满足跨学科技能。此外，这一步需要发展高级信息学技能，需要先进的计算知识，以生成模拟数据。第4步：将第2和第3步的结果应用于现实世界的流行病学问题。特别是，我们想研究癌症及其治疗对不断增长的癌症幸存者人群中随后发生不良呼吸结果风险的影响，该数据集收集了数百万人的数据。我们将使用匹配队列方法来解决研究问题，采用步骤3中构建的编码宏。这一步涉及各种优先技能的发展，如数据分析，大数据可视化和高性能计算。最后，这一阶段的一个关键目标将是患者群体参与涉及其自身健康的研究。为此，我们将与癌症幸存者设计一系列研讨会，专门关注患者和研究人员的互利。

An increasing number of studies are being conducted using health data generated when a patient interacts with any healthcare service (e.g a GP or hospital visit). This data can be used to conduct comparative epidemiological studies to investigate the risk of adverse health outcomes in diseased individuals (exposed group) compared those without a history of disease (unexposed group). Matched cohort methods are commonly used in this setting to select the unexposed group. In this method an exposed individual is matched with one or more unexposed individuals on one or more patient characteristics (e.g age and sex). In theory, this will allow a less biased comparison between the exposed and unexposed cohorts as matching will make the groups more similar to each other. However, this assumption has not been tested; little is known about when one should use a matched cohort design, how the matching should be conducted and what characteristics should be used to match individuals.The main aim of this project is to investigate a real-world epidemiological research question. Particularly, we aim to quantify the risk of respiratory diseases in individuals who have a history of cancer, compared to those with no history of cancer as part of a greater ambition of using big data to identify areas of disease prevention in cancer survivorsThe secondary aim is to evaluate the best methods to conduct and analyse matched cohort studies for clinical research of long-term outcomesThis project will be conducted in four steps:Step 1: Conduct a descriptive systematic literature review to systematically review the evidence on the association between cancer and subsequent respiratory disease. This step will be used to define the specific primary research respiratory outcomes of interest.Step 2: Conduct a narrative review to understand how matched cohort methods are used in epidemiological research and the current reasoning behind selecting this study design as well as identify the current research gaps.Step 3: Conduct a simulation study. We will generate an experimental simulated dataset which will act as a "controlled environment" in which we will be able to conduct experiments to test the assumptions that underpin the matched cohort design. The specific research questions that will be addressed will be identified from the narrative review (step 2). The aim of this stage is to create the most optimal matching strategies for matched cohort studies. The results of this stage will be used to develop open-access coding macros that can be used by other researchers to conduct matched cohort studies. This step will meet interdisciplinarity skills by developing data analytics technology that can be applied to many different research areas. Furthermore, this step will necessitate the development of high-level informatics skills by requiring advanced computational knowledge in order to generate simulation data.Step 4: Apply the findings of step 2 & 3 to a real-world epidemiological question. Particularly, we would like to investigate the impact of cancer and its treatment on the risk of developing subsequent adverse respiratory outcomes in the growing population of cancer survivors in a real-world health dataset that captures data from millions of individuals. We will use matched cohort methods to address the research question, employing the coding macros built in step 3. This step involves the development of a variety of priority skills such as data analytics, big data visualisation and high-performance computing. Finally, a key aim of this stage will be the involvement of patient groups in research that concerns their own health. To this end, we will design a series of workshops with cancer survivors focussed specifically on obtaining a mutual benefit for both patients and researchers.