Stephen completed undergraduate degrees in Mathematics at the University of Cambridge. He studied for a PhD in the MRC Biostatistics Unit, Cambridge, from 2008-11 working on methods for Mendelian randomization analysis under the supervision of Simon Thompson. He joined the Cardiovascular Epidemiology Unit in the Department of Public Health and Primary Care of the University of Cambridge in 2011. In 2013, Stephen was awarded a Wellcome Trust entry-level fellowship (Sir Henry Wellcome Post-doctoral Fellowship) to continue theoretical and applied work in the field of Mendelian randomization. In 2017, he moved to the MRC Biostatistics Unit on a Wellcome Trust/Royal Society intermediate fellowship (Sir Henry Dale Fellowship) to establish a research group in the MRC Biostatistics Unit, where he is now a Programme Leader. In 2023, he was given a Career Development Award by the Wellcome Trust to continue work in this area. He leads a small team of researchers split between the MRC Biostatistics Unit and the Cardiovascular Epidemiology Unit. He is always open to requests for collaboration, either on theoretical or applied Mendelian randomization projects.

Mendelian randomization: How can genetics guide the design of clinical trials?

When considering whether an exposure is a causal risk factor for an outcome, evidence from randomized trials is reliable but typically slow or impractical to gather, whereas evidence from conventional observational studies is often unreliable, as it is subject to bias from confounding and reverse causation. Mendelian randomization is an example of a quasi-experimental approach: it is analogous to a randomized trial, but relies on nature doing the randomization for us. Mendelian randomization can be implemented rapidly for a range of exposures to provide insights about causal relationships that can prioritize or deprioritize exposures for further investigation. This talk with discuss methods and examples that enable detailed Mendelian randomization analyses to inform the design of trials so that collection of randomized evidence can be as informative as possible: enabling trial interventions to target the right mechanism in the right population group at the right time.