D79 Mendelian Randomization 2027Info Location Attendee Categories Contact More Info Event Information
DescriptionAbstract This course covers the fundamental developments in Mendelian randomization and gives practical explanations about how to apply MR to applied research questions. This in person course will cover: one-sample, two-sample, pleiotropy robust methods, within family and drug target MR. Each method will be explained with applied examples, along with the recommended sensitivity analyses, validation, and specification tests to assess the plausibility of MR analysis. The course provides pre-readings and recorded lectures and aims to get students up to speed on how to undertake and publish high-quality Mendelian randomization studies.
Event Location
Attendee CategoriesResearchers.
Additional ItemsContactFor all queries in regards to this Course please contact the following :-
PLEASE ONLY CONTACT THE ONLINE STORE DIRECTLY IF YOU ARE EXPERIENCING PROBLEMS WITH YOUR DEBIT/CREDIT CARD PAYMENT, FOR ALL OTHER QUERIES RELATING TO THIS COURSE INCLUDING CANCELLATIONS THESE SHOULD BE DIRECTED TO THE CONTACT DETAILS ABOVE.
More InformationCourse Summary Introduction to Mendelian randomization One sample Mendelian randomization Two-sample Mendelian randomization Pleiotropy robust methods Multivariable MR MR for mediation and interaction Within family Mendelian randomization MR phenome-wide association studies Omics and drug target Mendelian randomization
Course leaders Emma Anderson Dylan Williams Neil Davies Amy Taylor
Guest Lecturer Eleanor Sanderson (Bristol, MRC IEU)
Who should apply? This course is aimed at people conducting applied epidemiological, medical and other quantitative research, from PhD students to experienced researchers interested in learning more about Mendelian randomization. Participants should have familiarity with applied statistical analysis and a MSc in statistics, data science, or other quantitative subject.
Pre-requisites: experience managing data and running regression models in either Stata or R (as well as knowledge on how to interpret model output).
Participants should have their own laptop with either Stata and R already installed.
Where: Wolfson Centre, UCL, Bloomsbury Recently Added | |||||||||||||||
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