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.
Kempf E. et al, (2018), British Journal of Cancer, 119, 1288 - 1296
Dutton P. and Holmes J., (2018), Pharmaceutical statistics
Bongard E. et al, (2018), BMJ open, 8
CHARIOT: Our first dose escalation trial using the Time to Event Continual Reassessment Method – Design, Difficulties, Delivery
Frangou E. et al, (2018)
Andrew MJ. et al, (2018), Developmental medicine and child neurology