There is unprecedented interest in estimating the safety and effectiveness of medicines. The widespread and near-real-time availability of real-world data (RWD) offers opportunities to quickly generate real-world evidence (RWE) to address these needs. Observational studies are vulnerable to many potential biases and translating RWD into high-quality RWE requires careful consideration of the scientific process including identification of fit-for-purpose data and selection of the most appropriate design, measurement, and analysis methods to mitigate potential biases.
The aim of this course is to equip students with the understanding and techniques to generate RWE on safety and effectiveness of medicines and overcome common sources of error, with practical applications using electronic health record data.
During this online course, students will:
- Develop their knowledge of concepts, study designs, and methods in pharmacoepidemiology
- Apply statistical techniques to generate real-world evidence using electronic health record data
- Understand biases and other sources of error that can occur in studies using real-world data, and identify and apply strategies to avoid them
Who should apply?
Individuals concerned with the safe and effective use of medicines should apply, especially those working in:
- The pharmaceutical industry who are involved in drug development, licensing, or surveillance
- Regulatory bodies who are involved in licensing & surveillance
- Academics interested in evaluating drug safety and effectiveness
- The health service who are involved in drug policy and decision making
The course is designed for individuals with a grounding in epidemiological methods and concepts and/or some prior knowledge of pharmacoepidemiology, especially those who have completed the Certificate in Pharmacoepidemiology & Pharmacovigilance.
Applicants will normally have a science, biomedical or biostatistical background, hold a second class honours degree of a United Kingdom university (or equivalent) in a science, medical, statistical or related subject and will have some post-graduate experience in the area of pharmacoepidemiology. Students should already have an understanding of methodological concepts such as bias and confounding. The course includes computer practicals conducted in Stata. Prior experience of Stata is not required; however, some statistical programming experience is strongly encouraged. It is not expected that applicants will actually be conducting pharmacoepidemiology studies. Applicants should have a good command of English.
Up to 30 participants will be accepted.
Advice for EU nationals
Students from the EU are very welcome at LSHTM and we would strongly encourage applications from EU nationals to this course.
What topics will you cover?
- Overview of study designs, including self-controlled designs, for pharmacoepidemiology
- The practicalities of planning a study, including study design and reproducible codelist building
- Measurements in pharmacoepidemiology - including outcomes, exposures, confounders, and issues of validation
- Sources of RWD around the world used for pharmacoepidemiology and the importance of identification of fit-for-purpose data
- Data-enabled trials
- Sources of error in pharmacoepidemiological studies, including methods for dealing with bias and confounding, with a particular focus on confounding by indication
- Thinking through causal relationships and regression model building
- Propensity scores, including high-dimensional propensity scores
- Quantitative bias analysis (QBA)
- Dealing with missing data
- Open science, reproducibility, and trusted research environments (TREs)
- Tools for distributed network pharmacoepidemiological studies
- Critical appraisal of published studies
- Overview of new methods and insights within pharmacoepidemiological research, including:
- Vaccines and confounding
- Target trials and trial replication
- Pharmacoepidemiology in the digital era
- Pharmacoequity
Course delivery
This is an online course. There is no face-to-face component during this course.
All teaching will be delivered online and consists of self-study material using a combination of pre-recorded lectures with synchronous interactive live sessions, including Q&A and panel discussions (based on pre-recorded content) and computer-based practical sessions. Computer-based practical sessions will use Stata software. Prior experience of Stata is not required; however, some statistical programming experience is strongly encouraged. Participants will be sent a link to access the course material before the course begins.
The course is tentatively set to run 9:30-16:30 BST daily, which includes a mixture of time to watch pre-recorded content and interactive live sessions (e.g., Q&A, computer practicals) where students have the opportunity to apply and discuss concepts introduced in the pre-recorded content. We will run 10 sessions over 5 days, separated into an AM and PM block each day of the week. Live Q&A sessions will be recorded and made available to students. Computer practicals will not be recorded.
Assessment
There is no formal assessment but a Certificate of Attendance will be provided at the conclusion of the course.
Teaching faculty
Prof Krishnan Bhaskaran, LSHTM
Dr Helen Blake, LSHTM
Mr Jeremy Brown, LSHTM
Ms Astrid Coste, LSHTM
Prof Will Dixon, University of Manchester
Prof Ian Douglas, LSHTM
Prof Stephen Evans, LSHTM
Dr Utibe Essien, University of Pittsburgh
Dr Susana Perez Gutthann, RTI Health Solutions
Dr Emily Herrett, LSHTM
Dr Masao Iwgami, University of Tsukuba/LSHTM
Dr Rohini Mathur, LSHTM
Mr Julian Matthewman, LSHTM
Dr Helen McDonald, LSHTM
Prof Neil Pearce, LSHTM
Prof Sallie Pearson, University of New South Wales
Prof Dani Prieto-Alhambra, University of Oxford
Dr Christopher Rentsch, LSHTM/Yale
Dr Anna Schultze, LSHTM
Dr Helen Strongman, LSHTM
Dr John Tazare, LSHTM
Dr Jemma Walker, LSHTM
Dr Angel Wong, LSHTM
Dr Kevin Wing, LSHTM
Clinical Practice Research Database (CPRD) Research Team
Applications are now closed. You can register your interest and we will let you know when applications reopen.
When applying for discounted fees, please include proof of student or LMIC status instead of your CV. LMIC status can be confirmed with a passport and proof of current residence. Charity status refers to individuals who are currently affiliated with a Charity.
Please read LSHTM's Admissions policies prior to submitting your application.