Modelling the evolution of disease risk in space and time (PhD)

Supervisors: Duncan Lee
Relevant research groups: Environmental Statistics, Biostatistics and Statistical Genetics

Mapping the spatial pattern in disease risk over a city or country is a common problem in epidemiology, and the primary aim is to determine which areas exhibit the greatest risks of disease. A recent extension to this field is to try and model how and to what extent the spatial risk surface changes over time. The motivation for this is to address questions such as: (1) on average across the study region, is the risk of disease getting more or less pronounced? and (2) in which areas of the study region are the disease risks getting worse? This project will develop statistical models to address these questions, and will apply them to map the evolution of important diseases, such as cancer and coronary heart disease, across regions of the UK.

PhD and MSc projects with Stephen Senn (MSc / PhD)

Supervisors: Stephen Senn
Relevant research groups: Biostatistics and Statistical Genetics, Statistical Modelling

A list of potential PhD and MSc projects with Stephen Senn can be found at http://www.senns.demon.co.uk/Research.html.

Modelling Genetic Variation (PhD)

Supervisors: Vincent Macaulay
Relevant research groups: Biostatistics and Statistical Genetics

Variation in the distribution of different DNA sequences across individuals has been shaped by many processes which can be modelled probabilistically, processes such as demographic factors like prehistoric population movements, or natural selection. This project involves developing new techniques for teasing out information on those processes from the wealth of raw data that is now being generated by high-throughput genetic assays, and is likely to involve computationally-intensive sampling techniques to approximate the posterior distribution of parameters of interest. The characterization of the amount of population structure on different geographical scales will influence the design of experiments to identify the genetic variants that increase risk of complex diseases, such as diabetes or heart disease.

Accounting for preferential sampling in air pollution and health studies (PhD)

Supervisors: Duncan Lee
Relevant research groups: Environmental Statistics, Biostatistics and Statistical Genetics

The health impact of exposure to air pollution is thought to reduce average life expectancy by six months, with an estimated equivalent health cost of 19 billion each year (from DEFRA). These effects have been estimated using statistical models, which quantify the impact on human health of exposure in both the short and the long term. However, the estimation of such effects is challenging, because individual level measures of health and pollution exposure are not available. Therefore, the majority of studies are conducted at the population level, and the resulting inference can only be made about the effects of pollution on overall population health. However, the data used in such studies are spatially misaligned, as the health data relate to a city or county, while the pollution concentrations are measured at individual locations. Furthermore, pollution monitors are typically located where concentrations are thought to be highest, known as preferential sampling, which is likely to result in overly high measurements being recorded. This project aims to develop statistical methodology to address these problems, and thus provide a less biased estimate of the effects of pollution on health than are currently produced.