Design and analysis of efficient clinical trials
I design and analyse clinical trials. Whenever appropriate, I use designs that are Bayesian and adaptive as they use data more efficiently. This means I write code to run simulations and play around with data which is the fun part of being a medical statistician. But it also improves the design of our trials, which is a good thing for patients and makes the fun worthwhile.
I specialise in early-phase Bayesian trials but am involved in trials of varying phase and disease, from cancer to flu, phase I to phase IV, model-based dose-finding to adaptive randomisation to biomarker driven. I am also involved in an individual patient data meta-analysis of beta-blockers and collaborate with the Centre for Suicide Research.
I obtained a BSc in Mathematics from Imperial College, London in 1993, and stayed to do a PhD in Bayesian model choice. I worked on various epidemiology projects in the Biostatistics department at Imperial before joining the Centre for Statistics in Medicine in 2011.
CHARIOT: Our first dose escalation trial using the Time to Event Continual Reassessment Method – Design, Difficulties, Delivery
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Cleland JGF. et al, (2018), Eur Heart J, 39, 26 - 35