Pint of Science - Statistics, linguistics and econometrics
Tuesday 14 May 2024
Time: 19:00 - 21:20
Bio
Emily is a PhD student in the Early Phase and Adaptive Trials team at the Institute of Cancer Research, London. Her current area of research lies in the incorporation of Patient Reported Outcomes (PROs) within early phase adaptive trial design and analysis. She has special interest in Bayesian Statistics and Monte Carlo methods.
Research interests
The primary endpoint for many Phase I oncology trials is the clinician-assessed maximum tolerated dose (MTD) which may be safely administered to a trial population. The MTD is often recommended as the Phase II dosage (RP2D).
As treatments such as immunotherapy emerge, it may no longer be the case that the MTD should become the RP2D. Escalating treatment dosage until the MTD is found may subject patients to unnecessary toxicities with little additional efficacy benefit. Relying solely on clinician-assessed adverse events to determine MTD offers scope for development.
The Patient-Reported Outcomes (PROs) version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) represents a 124-item questionnaire patients complete to record their personal experience of adverse events.
Whilst at the Turing, Emily will explore the implementation of PRO-CTCAE within a new dose-finding adaptive trial design. This design will combine both the clinician’s and patient’s assessment of drug toxicity to identify a recommended Phase 2 dosage.
What’s more, completing a weekly PRO-CTCAE questionnaire may be an unreasonable expectation for trial patients. Emily will also look to implement machine learning techniques to adaptively update questionnaires for each patient dependent on previous responses.
To ensure this work has tangible within-clinic impact, Emily will take inspiration from the Turing way and create lay summaries and apps to help explain her research to clinicians and patients alike.