Stratified blood pressure management: analysis of large-scale observational data and individual patient data meta-analyses of blood pressure lowering trials (Prof Kazem Rahimi and Dr. Dexter Canoy)
Stratified blood pressure management: analysis of large-scale observational data and individual patient data meta-analyses of blood pressure lowering trials
DESCRIPTION OF PROJECT
Despite the vast amount of evidence that has been accumulated to date, elevated blood pressure is still a major risk factor for death and disability worldwide. According to the most recent estimates, about 10 million people are dying each year as a result of elevated blood pressure. This is partly caused by residual uncertainties relating to treatment effects.
A DPhil student is sought to lead several individual-patient data meta-analyses from the Blood Pressure Lowering Treatment Trialists’ Collaboration (BPLTTC), in parallel to investigating important questions about the association of blood pressure and risks, and current treatment patterns using Big Data from routinely available clinical records as well as prospective epidemiological cohorts (e.g. UK Clinical Practice Research Database and the UK Biobank). The main research questions that this project will focus on are: (i) the safety of blood pressure lowering overall and by subgroups of patients; (ii) the effect of blood pressure lowering in important patient subgroups; (iii) description of pattern of care and its changes over time.
The BPLTTC is a collaborative meta-analysis of major randomized trials of blood pressure lowering drugs. It currently includes information from more than 45 trials with individual patient data from over 300,000 patients. The Collaboration has been highly successful in providing reliable answers to some of the key questions about the efficacy of blood pressure lowering but several others remain to be answered. In addition to this large randomized dataset, the student will have also access to routine healthcare records of several millions of patients. The combination of these datasets provide a unique opportunity to investigate questions of high relevance to public health and clinical practice. They also provide opportunities advancing methods for interrogation of such datasets.
This project would be suitable a candidate with a quantitative background (e.g. MSc in biostatistics, mathematics, epidemiology, or computer science) and interest in public health and cardiovascular medicine. The project is also suitable for a student with medical background, in which case some experience with statistical packages (preferably R) and good understanding of epidemiological concepts would be a strong advantage. For medical doctors, learning opportunities include training in advanced statistical methods, epidemiology and usage of statistical packages such as R. For non-clinical candidates, learning opportunities involve training in epidemiological study designs as well as advanced machine learning techniques for interrogation of some of the world’s largest and most complex datasets to address questions of high relevance to public health globally.
This project will be part of a new interdisciplinary programme entitled ‘Deep Medicine’ at the George Institute for Global Health. The research team provides expert individual supervision and support from several of experienced and enthusiastic researchers with backgrounds in clinical medicine, statistics, epidemiology, computer science and informatics. Further support in grant writing, high-impact scientific publications and career development will be provided. As well as the specific training detailed above, students will have access to a wide-range of seminars and training opportunities through the many research institutes and centres based in Oxford.