Ruth Young


Whilst studying for a Maths degree, I discovered my true interest lay in statistics. In my final year I pursued a dissertation in Bayesian statistics investigating Bayesian emulation and history matching; techniques for considering complex models with huge numbers of parameters. These techniques are applied in research areas including the growth of plants and galaxy formation. My PhD project gives me the chance to expand my knowledge of Bayesian statistical techniques and consider their application in a different context, focussing on the effectiveness of treatments to manage dementia. Through this, it fits into the bioinformatics and statistics theme in addition to the dementia theme of the BRC/U.

I applied for a BRC/U PhD studentship as I was keen to further expand my statistical knowledge, and was particularly interested in biostatistics as I wanted my work to make a difference to others. I liked the emphasis placed on translational research so that what we are learning can be put into practice to help people. Undertaking a Biomedical Research Centre (BRC) PHD studentship means I also benefit from being part of a cohort through which students across a range of disciplines, and at the same stage in their PhD, can engage with one another and take part in a comprehensive training programme.

On International Clinical Trials Day I assisted at a stall in King's College Hospital (KCH) where the purpose was to explain what a clinical trial encompasses, the history, and the importance of them. We also ran a mock clinical trial in order to facilitate a better understanding of what is involved and what can be achieved.


Mathematics (MMath), Durham University, UK

During my degree I had my first taste of statistical research when I did a summer internship with a company called Summit, who specialise in online advertising. My work involved the development of a model which used weather forecasts and searching trends in order to predict which products to promote when.

Borrowing strength from clinical trials for analysing longitudinal observational data: a Bayesian mixed model approach for investigating effectiveness of acetylcholinesterase inhibitors in dementia patients

The main aim of my project is to develop a statistical methodology which enables the combination of randomised controlled trial (RCT) and observational data, in order to provide better estimates of treatment effectiveness using all available evidence.

Each of these data sources comes with its own strengths and limitations. It is hoped that by combining them under a Bayesian framework it will be possible to reduce the bias in treatment effectiveness obtained when considering only observational data, whilst also increasing the generalisability of results in comparison to analyses which consider only RCT data.

This project falls under the bioinformatics and statistics theme. In addition, the project will apply the methodology to data concerning the use of acetylcholinesterase inhibitors in the management of dementia. The effectiveness of these drugs has been tested in a wide range of RCTs and the CRIS database provides a rich data source on their use in clinical practice.

I applied for this project as I enjoyed statistics and was keen to develop my statistical skills further, whilst applying them in an area with tangible benefits. Dementia is a major health concern in aging populations worldwide. In 2014 there were 835,000 people with dementia in the UK, and its management is a pressing concern to which I was keen to contribute.

Professor Sabine Landau and Mizanur Khondoker

Professor Robert Stewart (Third supervisor)


Bioinformatics & statistics

Our bioinformatics and statistics research integrates complex rich clinical data from patient records with large datasets from other areas including genomics & brain imaging to better understand psychiatric disorders.

Training & development

We aim to attract outstanding candidates with a range of experience and offer a variety of training schemes and secondment opportunities, spanning all academic career pathways.
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