Cardiovascular disease (CVD) gradually progresses over a period of time, and can lead to a cardiovascular disease event (“CVD event”) such as stroke or heart attack. There are several widely researched risk factors for CVD, such as smoking, diet, exercise, and stress (Perk et al., 2012). These risk factors can impact biomarkers like blood pressure and lipid levels, which can be measured by a primary care practitioner and are themselves risk factors (World Health Organization, 2021). The PREDICT cohort study (Wells et al., 2017, Pylypchuk et al., 2018) is comprised of the electronic health records (EHRs) of such CVD risk factor measurements, which were collected to assess the 5-year CVD risk of a patient in primary care. A risk prediction model previously developed for this population by Pylypchuk et al. (2018) is based on using only the most recent observations of these biomarkers.

Dynamic prediction is an alternative to this approach which updates risk predictions as measurements are collected, therefore using the entire history of these measurements. There are two main statistical frameworks that exist for performing dynamic prediction: the joint model and the landmark model. This thesis explores the use of dynamic prediction, and in particular the landmark model, to improve CVD risk prediction. Two types of landmark model for 5-year CVD risk are presented in this thesis, which were developed using the PREDICT cohort study dataset: one of these models the longitudinal data using a linear mixed effects (LME) model, and one which uses the last observation carried forward (LOCF) approach. It was found that these dynamic prediction models have some improvement in model performance over a “static” model which is similar to that developed by Pylypchuk et al. (2018). This thesis also presents the results of a simulation study to explore the difference between these two types of landmark models as the number of repeated measurements of the biomarkers increase, in particular finding that there is little difference in terms of model performance. Finally, this thesis presents an R package ‘Landmarking’ which allows the user to perform various analyses relating to the landmark model.