I am a Biometrika Trust Research Fellow and Senior Research Associate based at the MRC Biostatistics Unit, University of Cambridge. My current research is on error rate control for modern clinical trial designs which test multiple hypotheses simultaneously. My main areas of focus include:

• Trials where the multiple hypotheses tested have a natural (and possibly complex) grouping or ordering
• Designs which allow a potentially large number of new treatments to be added throughout the course of the trial
• Multi-arm trials where the probability of patients being randomised to the different treatment arms varies over time, based on the accumulated response data

Previously (from 2016-18) I was a Postdoctoral Statistician working with James Wason on the design and analysis of novel clinical trials. I did my PhD in the Biostatistics Unit under the supervision of Jack Bowden and Toby Prevost from 2013-16. My PhD project was on unbiased estimation in multi-arm drop-the-loser trials, with applications to diagnostic studies, genome-wide association studies and seamless phase II/III trials. Prior to this I did my undergraduate degree and masters in mathematics at the University of Cambridge.

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Research Background

In the classical framework of drug development, the response to experimental therapies is evaluated one treatment at a time within a homogenous patient group. However, this paradigm is increasingly shifting towards testing multiple related hypotheses simultaneously. Examples include:

• Multi-arm trials, which test multiple treatments in parallel
• Testing targeted therapies across multiple subgroups of patients, which is a key step towards the goal of personalised medicine
• Measuring multiple endpoints to help answer a range of clinical questions

At the same time, there continues to be a strong focus (particularly from a regulatory perspective) on guaranteeing control of suitable error rates. The new methodology that I am developing will enable more robust inference to be made from such clinical trials, and help ensure that only truly beneficial treatments are recommended to future patients on the basis of trial results.

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Google Scholar profile

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