Developing methods for model selection in causal health analyses.

This PhD project aims to conceptualise and implement an automated Bayesian system that can make decisions about statistical modelling in the context of causal health - these decisions include model form selection, variable selection, parameter fitting techniques and model evaluation methods / success metrics. Present developments are looking into causal inference, specifically the role of confounders and mediators in variable selection. We investigate different variable selection rubrics and confounder selection strategies to assess the suitability of different methods at forming causally valid statistical models. We seek to, from here, consider attempts at classifying variables (confounders, mediators or other types / properties) using Bayesian weighting where such categorisations are both subject to real-world interpretation and not typically identifiable from within the data. The broader project requires making use of larger more complicated data sources, plus priors for key variables. Lastly, we consider taking into account missing-ness (categorising types of missing data such as MCAR, MAR and MNAR), measurement error, variable categorisation and the underlying causal diagram (DAGs). The novelty of the research arises from the consideration of effect-interactions between variables and use of novel Bayesian techniques. The intended impact of the research would be on statistical models and their evaluation within medical papers, such as randomized control trials for new pharmaceuticals, wherein one typically employs a unit-block treatment model as a means of measuring treatment effects of different medicines on patients. This would improve the strength and statistical validity of papers in the medical literature, in turn improving decision making by medical practitioners in the real-world, thus improving patient outcomes. This project falls within the EPSRC mathematical sciences research area with a goal towards transforming health and healthcare. This project is supervised jointly by three professors: Kate Tilling (University of Bristol, Bristol Medical School), Jonathan Sterne (University of Bristol, Bristol Medical School) and Rhian Daniels (University of Cardiff, Department of Statistics). This PhD is funded jointly from three sources: 25% by NHS BRISTOL, 50% by EPSCRC COMPASS CDT, 25% by Faculty Funded PGR Studentship CDT funding. The project does not have industry partners as such, but Emma has worked alongside Southmead Hospital Bristol as part of her research.

The COMPASS Centre for Doctoral Training will have the following impact.

Doctoral Students Impact.

I1. Recruit and train over 55 students and provide them with a broad and comprehensive education in contemporary Computational Statistics & Data Science, leading to the award of a PhD. The training environment will be built around a set of multilevel cohorts: a variety of group sizes, within and across year cohort activities, within and across disciplinary boundaries with internal and external partners, where statistics and computation are the common focus, but remaining sensitive to disciplinary needs. Our novel doctoral training environment will powerfully impact on students, opening their eyes to not only a range of modern technical benefits and opportunities, but on the power of team-working with people from a range of backgrounds to solve the most important problems of the day. They will learn to apply their skills to achieve impact by collaborative working with internal and external partners, such as via our Rapid Response Teams, Policy Workshops & Statistical Clinics.

I2. As well as advanced training in computational statistics and data science, our students will be impacted by exposure to, and training in, important cognate topics such as ethics, responsible innovation, equality, diversity and inclusion, policy, effective communication and dissemination, enterprise, impact and consultancy skills. It is vital for our students to understand that their training will enable them to have a powerful impact on the wider world, so, e.g., AI algorithms they develop should not be discriminatory, and statistical methodologies should be reproducible, and statistical results accurately and comprehensibly communicated to the general public and policymakers.

I3. The students will gain experience via collaborations with academic partners within the University in cognate disciplines, and a wide range of external industrial & government partners. The students will be impacted by the structured training programmes of the UK Academy of Postgraduate Training in Statistics, the Bristol Doctoral College, the Jean Golding Institute, the Alan Turing Institute and the Heilbronn Institute for Mathematical Sciences, which will be integrated into our programme.

I4. Having received an excellent training, the students will then impact powerfully on the world in their future fruitful careers, spreading excellence.

Impact on our Partners & ourselves.

I5. Direct impacts will be achieved by students engaging with, and working on projects with, our academic partners, with discipline-specific problems arising in engineering, education, medicine, economics, earth sciences, life sciences and geographical sciences, and our external partners Adarga, the Atomic Weapons Establishment, CheckRisk, EDF, GCHQ, GSK, the Office for National Statistics, Sciex, Shell UK, Trainline and the UK Space Agency. The students will demonstrate a wide range of innovation with these partners, will attract engagement from new partners, and often provide attractive future employment matches for students and partners alike.

Wider Societal Impact

I6. COMPASS will greatly benefit the UK by providing over 55 highly trained PhD graduates in an area that is known to be suffering from extreme, well-known, shortages in the people pipeline nationally. COMPASS CDT graduates will be equipped for jobs in sectors of high economic value and national priority, including data science, analytics, pharmaceuticals, security, energy, communications, government, and indeed all research labs that deal with data. Through their training, they will enable these organisations to make well-informed and statistically principled decisions that will allow them to maximise their international competitiveness and contribution to societal well-being. COMPASS will also impact positively on the wider student community, both now and sustainably into the future.