Working on Phase 3 Clinical Trials Statistics in collaboration with Novartis.

Abigail graduated with an MMath from the University of Sheffield in 2017 where she spent her Master’s year focusing on Statistics. Her dissertation on MCMC methods applied to Emulators was the follow up to a summer research project about machine learning. During Abigail’s undergraduate degree, she developed an interest in Medical Statistics and completed a summer internship with Health Data Insight, a cancer data registry, analysing cancer recurrence times. Abigail enjoys playing the cello and participating in triathlon.

Research project title:
Phase 3 clinical trial statistics

Supervisor(s):
Chris Jennison, Lisa Hampson (Novartis)

Project description:
Clinical trials are composed of four stages, each of which has a different primary aim. This project focuses on Phase 3; the drug is already deemed safe, the dosage decided and the focus being efficacy and futility. The development of pharmaceuticals and medicines across all phases relies heavily on statistical methodology and accuracy, with Phase 3 summarised by a single hypothesis test for the difference in size of treatment effects. Patient safety and well-being are central to the design process. Abigail’s project considered group sequential trials, a mechanism introducing interim analyses and allowing for a trial to be stopped early for either efficacy or futility. The aspiration was that overall, less patients receive the less effective drug. For the analysis of clinical trials, a primary endpoint must be specified, this is the measurement of interest that is affected by the drug; for example this project focuses on survival or time-to-event as the primary endpoint. There has also been copious recent research on “biomarkers” which are underlying processes in the body that may be predictive or informative of the primary endpoint. Working with Novartis, Abigail researched a joint model for the two processes and investigated the gain to be made when biomarkers are included in a group sequential trial due to the increase in information.