Development and practical application of landmarking in studies of time-varying exposures and survival

理解个体特征对生存的影响是非常重要的，例如，对死亡率或疾病诊断率的影响。这些特征，我们统称为“症状”，可能是生活方式因素，接受的治疗或临床测量，如血压。研究个体随时间变化的暴露（“随时间变化的暴露”）及其与生存的关系往往是令人感兴趣的。通过了解这些关系，我们可以深入了解生物机制。这有助于开发新的治疗方法，并为公共卫生政策提供信息。了解随时间变化的暴露如何影响健康和生存也可以指导临床医生做出决策，为患者提供更个性化的治疗和预后。要研究随时间变化的暴露与生存之间的关系，需要长期收集大量人群的详细数据。这种类型的数据可以从患者数据登记处和电子健康记录中获得，例如从参与GP实践的记录中获得，以及从志愿者收集的生物材料库（生物库）。这个奖学金将使我能够开发统计方法，使研究人员能够进行分析，回答一系列关于随时间变化的暴露和生存之间的关系的重要问题。有一些不同的问题，我们可能希望尝试调查，包括：-对于一个人与特定的诊断和知道该人的时间变化的暴露测量到目前为止，他们的生存的预测概率是什么至少5年从现在开始？- 哪些暴露对生存率有因果影响，因此是最重要的治疗目标？在随时间变化的暴露中，随着时间的推移，不同的模式对生存的影响是什么？当有许多变量需要同时考虑时，解决这些问题变得特别复杂，这些变量都随着时间的推移而相互影响。为了试图建立我们主要感兴趣的测量的因果效应，试图理清不同的随时间变化的暴露与生存的关系是一个巨大的挑战。为了解决上述问题，目前需要使用完全不同的复杂统计技术。这意味着研究人员不鼓励使用它们，我们无法从现有数据中获得最佳信息。我在这项研究中的目的是开发基于一种称为“地标”的方法的统计方法，以提供一种以类似的方式解决上述问题类型的方法，并且这种方法对包括临床医生在内的广泛的研究人员来说是直观和可访问的。我的部分工作将是在统计方法中纳入处理实际使用数据所带来的困难的能力。我将重点讨论两个特殊的挑战，即在某些时间点，某些个体的测量值缺失，以及许多暴露量的测量存在一定程度的误差。对我来说，重要的是提供广泛使用的方法。我将提供可用于实现我的方法的软件代码。应用开发的方法将有重要的应用在电子健康记录数据，因此将能够有许多健康状况，可以使用这些数据库进行研究的影响。作为这个奖学金的一部分，我将应用开发的统计方法来研究有关囊性纤维化患者的生存问题，使用来自大型患者登记处的数据。

Context It is of great importance to understand the effects of the features of an individual on their survival - that is, for example, on mortality or disease diagnosis rates. These features, which we refer to collectively as 'exposures', may be lifestyle factors, treatments received, or clinical measurements such as blood pressure. It is often of interest to study exposures which are changing in an individual over time ('time-varying exposures') and how they relate to survival. By understanding these relationships we can gain insight into biological mechanisms. This helps towards the development of new treatments and can inform public health policy. Understanding how time-varying exposures impact on health and survival can also guide clinicians in their decision making, enabling more individualised treatment and prognosis for patients.To study relationships between time-varying exposures and survival requires detailed data on large populations collected over long time periods. Data of this type can be obtained from patient data registries and electronic health records, which are for example obtained from records of participating GP practices, and banks of biological material collected from volunteers (biobanks).AimsTo analyse the types of data mentioned above requires complex statistical methods and this is still a developing area of research. This fellowship would enable me to develop statistical methods which enable researchers to perform analyses which answer a range of important questions regarding the relationships between time-varying exposures and survival. There are a number of different questions which we may wish to try to investigate, including:- For a person with a particular diagnosis and knowing that person's time-varying exposure measurements up to the present time, what is their predicted probability of surviving up to at least 5 years from now? - Which exposures are having a causal impact on survival and therefore would be the most important targets for treatment?- What are the impacts on survival of different patterns over time in time-varying exposures?Addressing these questions becomes especially complicated when there are lots of variables to be considered simultaneously which are all influencing one another over time. It is a big challenge to try to disentangle the associations of different time-varying exposures with survival in order to try to establish causal effects of the measurements we are primarily interested in. To address the above questions currently requires the use of quite different complex statistical techniques. This means that researchers are discouraged from using them and we do not get the best information out of the available data. My aim in this research is to develop statistical methods based on an approach called 'landmarking' to provide a way of addressing the types of questions given above in a similar way and in a way which is intuitive and accessible to a wide range of researchers, including clinicians. Part of my work will be to incorporate into the statistical methods the capacity to handle the difficulties which come from using data in practice. I will focus on two particular challenges, which are the occurrence of missing measurements in some individuals at some time points, and the fact that many exposures are measured with some degree of error. It is important to me to provide methods which are widely accessible. I will provide software code which can be used to implement my methods. ApplicationsThe methods developed will have important applications in electronic health records data and will therefore be able to have an impact on the many health conditions which can be studied using these databases. As part of this fellowship I will apply the statistical methods developed to study questions concerning the survival of people with cystic fibrosis using data from a large patient registry.