The London School of Hygiene & Tropical Medicine is pleased to invite applications for two PhD studentships in pharmacoepidemiology, funded for 3.5 years by GlaxoSmithKline, starting in January or April 2021.
The award will cover:
- a tax free stipend of GBP 22,946 per year; and
- tuition fees (at the home/EU student rate).
Please note: the award is only available to candidates who meet the eligibility requirements for home/EU fees (see Eligibility Requirements below).
The studentships will be based in the Faculty of Epidemiology and Population Health. The Faculty is multi-disciplinary and encompasses epidemiologists, medical statisticians, medical demographers, nutritionists, social scientists and public health practitioners. The successful candidates will also spend time at the offices of GlaxoSmithKline in West London.
There is a choice of possible topic areas, described below. The exact focus of the PhD will be developed with the successful candidate and will depend on their interests and prior expertise and interests. Applicants are asked to contact the supervisor of the project they are most interested in for an informal discussion prior to applying.
Overarching theme: Optimising methodology to minimise bias in real world studies, increasing the acceptability of real world evidence for decision making.
- Drug Safety Signal Detection Using Electronic Health Records
Supervisor – Ian Douglas (email@example.com)
Internationally, there is no agreed best approach to signal detection in electronic health records and this project offers the opportunity to have a global impact on routine drug safety practices. Identifying signals of possible drug safety problems has long been practised using databases of spontaneous reports. In recent years, attention has been paid to the possibility of using databases of electronic health records for the same purpose, with the aim of determining whether such records can be routinely used for drug safety monitoring. This PhD will extend our previous research on the use of Self Controlled Case Series for signal detection (Zhou, X., Douglas, I.J., Shen, R. and Bate, A., 2018. Signal detection for recently approved products: adapting and evaluating self-controlled case series method using a US claims and UK electronic medical records database. Drug safety, 41(5), pp.523-536.), which showed better signal detection performance for some outcomes than others; for example SCCS performs less well with slow onset outcomes such as hypertension and hyperlipidemia. This project will look to further understand which outcomes SCCS shows best value for signal detection in primary care electronic health record data, and which least. For those where it is least valuable such as very late onset outcomes, cohort based methods with propensity scores, and other approaches could be explored. This will build on the research of amongst others Pottegard et al (Pottegard, A, Friis, S, Christensen, Rd. Identification of Associations Between Prescribed Medications and Cancer: A Nationwide Screening Study. EBioMedicine 2016; 7: 73–79.).
The goal will be to show how to choose between conventional study design and self controlled methods for signal detection in electronic health data, and may also compare against signal detection using spontaneous reports. You will look to understand the basis for differential performance: for example by outcome or exposure and comparator type. When considering cohort-based methods, selection of comparator drugs is critical, and can raise specific difficulties for newly launched drugs. This is because prescribing patterns for recently launched drugs can evolve rapidly and can be hard to characterise, making choice of comparator difficult. We will also look to explore whether/how placebo or other comparator data from historical RCTs might be used as a comparator in such circumstances.
- The role of polypharmacy before and after cancer diagnosis
Polypharmacy, commonly defined as the concurrent use of five or more chronic medications, is increasingly prevalent, and is a strong predictor of drug-drug interactions and drug toxicity. Over the last decade, polypharmacy rates nearly doubled to >50% among adults ≥65 years continues to increase. Long-term and late effects of cancer, combined with ongoing management of other chronic conditions, make cancer survivors particularly vulnerable to polypharmacy and its adverse effects. Yet, existing research on polypharmacy is problematic largely because of small sample sizes, non-standardised definitions of polypharmacy, and inadequate adjustment for confounding by indication. Over recent years, advanced methods that account for time-varying confounding by indication have grown in popularity and accessibility.
This project will investigate and compare methodological challenges associated with studying polypharmacy with a clinical focus on the cancer survivorship setting. Definitions of polypharmacy and their implementation using electronic health records data will be explored. Statistical methods to account for confounding by indication will be compared, including propensity score and g-estimation based models. Guidelines on identifying polypharmacy, designing studies involving polypharmacy, and choosing appropriate statistical analysis methods will be developed. Other intended outputs from the project include the development of open-source tools and algorithms for other researchers to use when measuring polypharmacy in routinely collected data. Electronic health record data from the largest healthcare systems in each of the UK and US will be made available to compare and validate findings across country and healthcare system.
- Investigating bias in COVID-19 drug-related research; developing tools for quantitative bias analysis
Supervisor: Ian Douglas (firstname.lastname@example.org)
The current COVID-19 pandemic has led to an extraordinary and rapid response from the scientific research community, and a large volume of research addressing questions of urgent public health importance. Early questions were raised about the possible harmful or beneficial effects of medications in either preventing or treating infection with SARS-CoV-2, either harmful or beneficial. Examples include hydroxychloroquine, NSAIDs and antihypertensives. Initial evidence largely came from non-interventional studies, with concerns about data quality, and the role of various biases in possibly explaining the findings. Over recent years, interest has grown in the field of quantitative bias analysis, whereby investigators or readers can estimate the extent to which unknown or unadjusted bias may account for results. The role of confounding in this area has received the most attention to date, but the role of selection bias and misclassification is still often overlooked. This project will take COVID-19 medication related studies as an evidence core in which to investigate the best forms of quantitative bias analysis, and to develop freely available tools for other researchers to use in applying these methods. A novel research question will then be addressed as a test case in which the methods can be applied.
Applicants must hold, or expect to obtain before the start of the PhD, a relevant MSc (such as Epidemiology or Medical Statistics) awarded with good grades, or have a combination of relevant qualifications and experience which demonstrates equivalent ability and attainment.
The PhD programme
Students will be mentored by their supervisor and an Advisory Committee consisting of at least two other academics, who can be from outside the School. Each Advisory Committee will also have at least one epidemiologist or statistician from GlaxoSmithKline. Students are expected to take part in the academic life of their department and can also be members of other Academic Centres - e.g. Centre for the Mathematical Modelling of Infectious Diseases, the Clinical Trials Unit, the Malaria Centre, The TB Centre, the MARCH centre for maternal and child health, and the Centre for Statistical Methodology. All research seminars and journal clubs are open to PhD students from across the School. Students are able to take up to four Master’s level Study Modules per academic year, subject to approval from their supervisor.
Support for research students’ future career development is covered through the supervision process, through the Transferable Skills Programme (in the School and the Bloomsbury Postgraduate Skills Network) and the School’s Careers Service. Also important for career development are the opportunities for students to network and establish professional contacts. The School also facilitates national and international conference attendance by students in the later stages which provides networking opportunities.
How to apply
Applicants should select one project from the list and provide the following documents:
- A 2-page curriculum vitae, including details of their academic achievements to date and the names of 2 referees (at least one of whom should be able to comment on your academic ability)
- A research proposal of up to 1,000 words*
- A covering letter saying why they are interested in undertaking a PhD in this area at LSHTM
- Copies of the transcripts from their undergraduate and postgraduate qualifications
- An eligibility form
*The research proposal should identify a specific research question or hypothesis, expanding on one of the topics listed on the website, summarise the relevant background information (with no more than five key references) and should outline an appropriate research methodology by which the question can be addressed.
Applications should be submitted by email to EPH.FRDD@lshtm.ac.uk.
Please state clearly in the heading and text of the email that this is an application for a pharmacoepidemiology studentship.
Only applications in the correct format will be considered.
The deadline for applications is 3 September 2020, 10.00 am BST.