Project description
PharmAlliance Research Clusters for Doctoral Training (PARCDT) Programme
PharmAlliance is a strategic partnership between three global leaders: UNC Eshelman School of Pharmacy at the University of North Carolina at Chapel Hill, Monash University Faculty of Pharmacy and Pharmaceutical Sciences, and UCL School of Pharmacy at University College London (https://www.pharmalliance.org/). This PhD programme, which is funded by the PharmAlliance Research Clusters for Doctoral Training (PARCDT) scheme, aims to bring together the three outstanding pharmacy schools to develop a cohort of pharmacoepidemiology scientists with world-class expertise in areas of strategic importance to health research. The successful candidate will be based in the Pharmacoepidemiology and Medication Safety Research Cluster at UCL School of Pharmacy. The candidate will be encouraged and supported to formulate research activities that bring a variety of experts together from across the PharmAlliance network, as well as having the opportunity to lead and participate in a cluster of activities to develop a community among a cohort of pharmacoepidemiology PhD students across the PharmAlliance sites.
Project Description
Worldwide, it is estimated that more than 55 million people are living with dementia. With the ageing population, this number is projected to increase to 139 million by 2050, making dementia a pressing global health challenge. Routinely collected observational data (“Real-World” data) such as electronic health records, administrative claims, and disease-specific registries provide a rich source of clinical information to aid our understanding of the progression of diseases, utilisation of medication, and the impact of public health interventions on a large population. This PhD project will exploit the potential of the large Real-World data to generate new evidence for multimorbidity and medication overload in people living with dementia. The project aims to generate high-quality real-world evidence that can help to reduce the evidence-practice gap for how to best optimize medication use in the clinically heterogeneous population of individuals living with dementia, who are often excluded from randomised clinical trials.
Main methods and techniques
The student is expected to use observational study techniques to work on the big data from large electronic health databases. The student will have access to training courses to become familiar with various study designs that can be used and will gain experience in epidemiological study methods, data analysis, systematic reviews as well as writing papers for peer-reviewed journals. Attendance at formal training courses will be encouraged.
Person specification
Essential Criteria
- A Bachelor’s degree (minimum 2:1) and/or a Master's degree (preferably with a merit or distinction) in pharmacy, epidemiology, public health, statistics, or an allied discipline
- Interest in applications of quantitative research methods
- Ability to organise and prioritise workload
- Ability to work as part of a multinational and multidisciplinary team
- Excellent verbal and written communication skills (ranging from informal 1:1 discussions to formal presentations)
Desirable
- Experience in data analysis and the use of statistical software such as SAS and R
- Experience in searching and reviewing research literature
Preferred postgraduate or other work experience, if relevant: Master’s degree in epidemiology, public health, or an allied discipline.
Applicants will also need to meet UCL MPhil/PhD entry and English Language requirements. See link for further details https://www.ucl.ac.uk/prospective-students/graduate/research-degrees/pharmacy-mphil-phd
The preferred start date for the successful applicant is October 2022.
How to apply
Applications must include a CV, a personal statement detailing how your skillset and background match the requirements for pursuing a PhD in pharmacoepidemiology, and the contact details of two referees. Applications should be emailed to Ms Michelle Ward at [Email Address Removed]
The supervisors for this project are Dr Wallis Lau and Professor Li Wei.
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