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

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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.