This PhD focusses on development and application of statistical methods. Specifically, the student will investigate how linked disease registry data can be suitably matched to control populations using published statistics and large-scale EHRs (e.g. from primary care) to address questions of excess rates of comorbidity following diagnosis and intervention for CVD. The student will build on research undertaken in the Biostatistics Research Group in relative survival analyses and multistate modelling.
Dr Michael Sweeting
Professor Paul Lambert
As a result of improvements in treatments and management of cardiovascular disease (CVD), patients are now living longer but with a potentially higher risk of developing comorbidities. A large population of patients now live with both CVD and cancer but there is limited data concerning the interplay between these two conditions. Control populations are required to address questions of excess rates of disease in a cohort. At an individual level, it is not possible to ascertain whether a subsequent event (e.g. a cancer diagnosis) is caused by the patient having CVD or not. However, at an aggregate level it is possible to compare the rate at which events occur in the CVD cohort with the rate in a control group free of CVD.
In this project the student will investigate different approaches to using control group data, either utilising published population statistics or using individual patient data from large scale electronic health records, for estimating excess rates of morbidity and mortality. The advantages and disadvantages of each approach will be formally investigated. Survival analysis methods, including the use of multistate and relative survival models, will be used to calculate clinically useful quantities such as expected years of life lost due to comorbidities. The student will be encouraged to develop free-to-use software and provide web implementation of clinically relevant metrics to disseminate their research.
This project will provide the student with a comprehensive Biostatistical and Epidemiological scientific education, including training in Statistical Programming and
Applied Research. The work will be conducted in the Biostatistics Research Group at the University of Leicester, with direct supervisory input from Dr Michael Sweeting, and second supervisor Professor Paul Lambert.