This post offers an excellent opportunity for an experienced Research Associate to work with the DISCOVERY research team in the School of Social and Community Medicine. The DISCOVERY research programme is led by Professor Willie Hamilton and aims to identify and develop better ways to diagnose cancer. This post relates to Discovery theme 3.2, led by Professor Tony Ades and will model investigative pathways for cancer diagnosis. The aim of the study is to identify the most cost-effective investigative pathways for patients presenting to GPs with symptoms that could trigger further investigations for cancer, including the most cost effective thresholds for referral. The post will involve construction of a decision model to compare the costs and effects of alternative screening and/or referral strategies, and probabilistic cost-effectiveness analysis. There will be a need to work in close collaboration with clinical experts, and to become familiar with the epidemiology of several cancers and trends in survival rates. There will be opportunities to apply modern methods of Bayesian evidence synthesis and Expected Value of Information methods.
• Experience &/or good understanding of health economic modeling • Experience &/or good understanding of medical statistics • Evidence of data analysis using common statistical software package(s), especially R or STATA • Competence with word processing and data management software: especially WORD, Excel, Powerpoint • Evidence of clear and concise scientific writing • Experience of managing own workload • Willingness to undertake relevant training • Experience managing own workload