 |
 |
ASTRAZENECA PhD STUDENTSHIP AT THE UNIVERISTY OF LEICESTER Applications are invited for a three-year full-time PhD studentship in the Centre for Biostatistics and Genetic Epidemiology at the University of Leicester starting 1 October 2006. The project is entitled "Modelling phenotypic variation in large medical databases using latent variables" and will look at theoretical and practical problems that arise when latent variable and latent class models are used with large clinical databases. The initial application will be to the study of patterns of disease in a database of patients with difficult to treat asthma. A brief description of the project is given at the end of this email. This studentship is open to EU citizens. Applicants should be interested in working on medical applications of statistics and have at least a 2(i) Honours degree or a recognised postgraduate qualification containing a substantial element of statistics. The Centre for Biostatistics and Genetic Epidemiology is part of the Department of Health Sciences within the University's Medical School. For more information about the Department, see our website at www.hs.le.ac.uk The studentship is funded by AstraZeneca and the PhD student will have the opportunity to visit AstraZeneca for a short period each year, although they will be based at the University of Leicester. For written details on the studentship and the department please contact Joy Fox at: [log in to unmask] . If you have specific questions concerning the studentship or the project, please email the main PhD supervisor, Professor John Thompson at [log in to unmask] An application form can be downloaded from the University of Leicester Graduate School at www.le.ac.uk/gradschool . Alternatively, email [log in to unmask], phone +44 (0)116 252 3206 or write to: Mrs J Fox Centre for Biostatistics and Genetic Epidemiology Department of Health Sciences University of Leicester 22-28 Princess Rd West Leicester LE1 6TP The closing date for applications is Friday 11th August 2006. Brief Project Description: Modelling phenotypic variation in large medical databases using latent variables The Leicester difficult asthma cohort was collected by clinicians in Leicester and contains nearly 400 people recruited through a difficult asthma clinic. There is extensive baseline information on these patients and they are now being followed longitudinally with collection of data on lung function and airway inflammation. The investigators have commented on the degree of phenotypic variation in their patients, that is the large variation in the way that the disease manifests, and they have questioned whether this is due to the range of disease severity or to the presence of subgroups of patients with diseases that follows different natural histories. This will be investigated using latent variable models. Latent variable methods assume that underlying a condition there are unmeasured quantities, such as disease severity or diagnostic grouping, that influence the measured data. This approach is the basis for factor analysis, mixture modelling and a whole range of other statistical techniques. The technique is commonly applied to classification problems in medicine particularly in Psychiatry were disease classification is a major problem. However, these studies are almost exclusively based on the analysis of relatively small numbers of variables. There have been very few applications to very large databases similar to the difficult asthma database. The fitting of latent variable models is not a trivial exercise and if the models are to be flexible enough to cope with binary, categorical and continuous measures, missing data, covariates and longitudinal measurements then fitting is complex. Recently the statistical literature has included a number of articles that have looked at the use of Markov chain Monte Carlo (MCMC) methods for the fitting of latent variable models. MCMC algorithms are extremely flexible. The clinical investigators are keen to work with the PhD student to help them develop meaningful models. This project will investigate the use of latent variable analysis with large medical databases and as such will be of interest well beyond the specific asthma application. The methods developed will be applied to other locally collected clinical databases.